![Detection of the Prodromal Phase of Bipolar Disorder from
Psychological and Phonological Aspects in Social Media
Yen-Hao Huang
National Tsing Hua University
Hsinchu, Taiwan
yenhao0218@gmail.com
Lin-Hung Wei
National Tsing Hua University
Hsinchu, Taiwan
adeline80916@gmail.com
Yi-Shin Chen
National Tsing Hua University
Hsinchu, Taiwan
yishin@gmail.com
ABSTRACT
Seven out of ten people with bipolar disorder are initially
misdiagnosed and thirty percent of individuals with bipolar
disorder will commit suicide. Identifying the early phases of
the disorder is one of the key components for reducing the
full development of the disorder. In this study, we aim at
leveraging the data from social media to design predictive
models, which utilize the psychological and phonological fea-
tures, to determine the onset period of bipolar disorder and
provide insights on its prodrome. This study makes these dis-
coveries possible by employing a novel data collection process,
coined as Time-specific Subconscious Crowdsourcing, which
helps collect a reliable dataset that supplements diagnosis
information from people suffering from bipolar disorder. Our
experimental results demonstrate that the proposed models
could greatly contribute to the regular assessments of people
with bipolar disorder, which is important in the primary care
setting.
KEYWORDS
Bipolar Disorder Detection, Mental Disorder, Prodromal
Phrase, Emotion Analysis, Sentiment Analysis, Phonology,
Social Media
1 INTRODUCTION
Bipolar disorder (BD) is a common mental illness charac-
terized by recurrent episodes of mania/hypomania and de-
pression, which is found among all ages, races, ethnic groups
and social classes. The regular assessment of people with
BD is an important part of its treatment, though it may be
very time-consuming [21]. There are many beneficial treat-
ments for the patients, particularly for delaying relapses. The
identification of early symptoms is significant for allowing
early intervention and reducing the multiple adverse conse-
quences of a full-blown episode. Despite the importance of
the detection of prodromal symptoms, there are very few
studies that have actually examined the ability of relatives to
detect these symptoms in BD patients. [20] For the purpose
of early treatment, the challenge leads to: how to identify
the prodrome period of BD. Current studies are thus
aimed at detecting prodromes and analyzing the prodromal
symptoms of manic recurrence in clinics.
With regards to the symptom of social isolation, people
are increasingly turning to popular social media, such as
Facebook and Twitter, to share their illness experiences or
seek advice from others with similar mental health conditions.
As the information is being shared in public, people are
subconsciously providing rich contents about their states
of mind. In this paper, we refer to this sharing and data
collection as time-specific subconscious crowdsourcing.
In this study, we carefully look at patients who have been
diagnosed with BD and who explicitly indicate the diagnosis
and time of diagnosis on Twitter. Our goal is to both predict
whether BD rises on a given period of time, and to discover
the prodromal period for BD. It’s important to clarify that
our goal doesn’t seek to offer a diagnosis but rather to make
a prediction of which users are likely to be suffering from the
BD. The main contributions of our work are:
• Introducing the concept of time-specific subconscious
crowdsourcing, which can aid in locating the social
network behavior data of BD patients with the corre-
sponding time of diagnosis.
• A BD assessment mechanism that differentiates be-
tween prodromal symptoms and acute symptoms.
• Introducing the phonological features into the assess-
ment mechanism, which allows for the possibility to
assess patients through text only.
• An automatic recognition approach that detects the
possible prodromal period for BD.
2 RELATED WORK
Social media resources have been widely utilized by researchers
to study mental health issues. The following literature em-
phasizes on data collection and feature engineering, including
subject recruitment, manual data collection, data collection
applications, keyword matching, and combined approaches.
The clinical approach for mental disorders and prodrome
studies are also discussed in this section.
Subject recruitment: Based on customized question-
naires and contact with subjects, Park et al. [15] recruited
participants for the Center for Epidemiologic Studies Depres-
sion scale(CES-D) [17] and provided their Twitter data. By
analyzing the information contained in tweets, participants
were divided into normal and depressive groups based on
their scores on CES-D. An approach like this one requires ex-
pensive costs to acquire data and conduct the questionnaire.
Manual and automatic data collecting: Moreno et
al. [14] collected data via the Facebook profiles of college stu-
dents reviewed by two investigators. They aimed at revealing
the relationship between demographic factors and depression.
Similarly, in our work, we invest on manual efforts to collect
and properly annotate our dataset. In addition, there are
many applications built on top of social networks that provide
free services where users may need to input their credentials
arXiv:1712.09183v1[cs.IR]26Dec2017](https://image.slidesharecdn.com/digitalhealthcare-intheperspectiveofmentaldisorder-180420075555/85/slide-56-320.jpg)
![Detection of the Prodromal Phase of Bipolar Disorder from
Psychological and Phonological Aspects in Social Media
Yen-Hao Huang
National Tsing Hua University
Hsinchu, Taiwan
yenhao0218@gmail.com
Lin-Hung Wei
National Tsing Hua University
Hsinchu, Taiwan
adeline80916@gmail.com
Yi-Shin Chen
National Tsing Hua University
Hsinchu, Taiwan
yishin@gmail.com
ABSTRACT
Seven out of ten people with bipolar disorder are initially
misdiagnosed and thirty percent of individuals with bipolar
disorder will commit suicide. Identifying the early phases of
the disorder is one of the key components for reducing the
full development of the disorder. In this study, we aim at
leveraging the data from social media to design predictive
models, which utilize the psychological and phonological fea-
tures, to determine the onset period of bipolar disorder and
provide insights on its prodrome. This study makes these dis-
coveries possible by employing a novel data collection process,
coined as Time-specific Subconscious Crowdsourcing, which
helps collect a reliable dataset that supplements diagnosis
information from people suffering from bipolar disorder. Our
experimental results demonstrate that the proposed models
could greatly contribute to the regular assessments of people
with bipolar disorder, which is important in the primary care
setting.
KEYWORDS
Bipolar Disorder Detection, Mental Disorder, Prodromal
Phrase, Emotion Analysis, Sentiment Analysis, Phonology,
Social Media
1 INTRODUCTION
Bipolar disorder (BD) is a common mental illness charac-
terized by recurrent episodes of mania/hypomania and de-
pression, which is found among all ages, races, ethnic groups
and social classes. The regular assessment of people with
BD is an important part of its treatment, though it may be
very time-consuming [21]. There are many beneficial treat-
ments for the patients, particularly for delaying relapses. The
identification of early symptoms is significant for allowing
early intervention and reducing the multiple adverse conse-
quences of a full-blown episode. Despite the importance of
the detection of prodromal symptoms, there are very few
studies that have actually examined the ability of relatives to
detect these symptoms in BD patients. [20] For the purpose
of early treatment, the challenge leads to: how to identify
the prodrome period of BD. Current studies are thus
aimed at detecting prodromes and analyzing the prodromal
symptoms of manic recurrence in clinics.
With regards to the symptom of social isolation, people
are increasingly turning to popular social media, such as
Facebook and Twitter, to share their illness experiences or
seek advice from others with similar mental health conditions.
As the information is being shared in public, people are
subconsciously providing rich contents about their states
of mind. In this paper, we refer to this sharing and data
collection as time-specific subconscious crowdsourcing.
In this study, we carefully look at patients who have been
diagnosed with BD and who explicitly indicate the diagnosis
and time of diagnosis on Twitter. Our goal is to both predict
whether BD rises on a given period of time, and to discover
the prodromal period for BD. It’s important to clarify that
our goal doesn’t seek to offer a diagnosis but rather to make
a prediction of which users are likely to be suffering from the
BD. The main contributions of our work are:
• Introducing the concept of time-specific subconscious
crowdsourcing, which can aid in locating the social
network behavior data of BD patients with the corre-
sponding time of diagnosis.
• A BD assessment mechanism that differentiates be-
tween prodromal symptoms and acute symptoms.
• Introducing the phonological features into the assess-
ment mechanism, which allows for the possibility to
assess patients through text only.
• An automatic recognition approach that detects the
possible prodromal period for BD.
2 RELATED WORK
Social media resources have been widely utilized by researchers
to study mental health issues. The following literature em-
phasizes on data collection and feature engineering, including
subject recruitment, manual data collection, data collection
applications, keyword matching, and combined approaches.
The clinical approach for mental disorders and prodrome
studies are also discussed in this section.
Subject recruitment: Based on customized question-
naires and contact with subjects, Park et al. [15] recruited
participants for the Center for Epidemiologic Studies Depres-
sion scale(CES-D) [17] and provided their Twitter data. By
analyzing the information contained in tweets, participants
were divided into normal and depressive groups based on
their scores on CES-D. An approach like this one requires ex-
pensive costs to acquire data and conduct the questionnaire.
Manual and automatic data collecting: Moreno et
al. [14] collected data via the Facebook profiles of college stu-
dents reviewed by two investigators. They aimed at revealing
the relationship between demographic factors and depression.
Similarly, in our work, we invest on manual efforts to collect
and properly annotate our dataset. In addition, there are
many applications built on top of social networks that provide
free services where users may need to input their credentials
arXiv:1712.09183v1[cs.IR]26Dec2017
Wordcloud
Features(#DIM) 2 mths 3 mths 6 mths 9 mths 12 mths
AG(2)
0.475 0.503 0.445 0.434 0.383
Pol(5)
0.911 0.893 0.843 0.836 0.803
Emot(8)
0.893 0.895 0.908 0.917 0.896
Soc(4)
0.941 0.913 0.845 0.834 0.786
LT(1)
0.645 0.589 0.554 0.504 0.513
TRD(1)
0.570 0.638 0.626 0.615 0.654
Phon(8)
0.889 0.880 0.802 0.838 0.821
Table 2: Average Precision of Single Feature Perfor-
mance
Age and Gender
Mood Polarity Features
Emotional Score
Social Feature
Late Tweet Frequency
Tweet Rate Difference
Phonological Feature
Diagnosed time !
" months
" = 2 months
Figure 1: Illness Period Modeling
features are introduced: (1) Word-level features and BD
Pattern of Life features.
3.4.1 Word-level Features. With respect to the linguis-
tic features for BD, the Character n-gram language fea-
tures(CLF) and LIWC metrics are designed to capture it.
The CLF utilizes n-grams to measure the comment words
or phrases used by users. The tf-idf is utilized in our score-
calculating method, the tf is the frequency of an n-gram and
the document d of df is defined as each particular twitter
user k. The formula for the tf-idf is thus given as:
tfidf
(k,⌧,↵)
vn = freq
(k,⌧,↵)
vn ⇥ log
K
1 + freq
(K,⌧,↵)
vn
(1)
The freq
(k)
vn is the frequency of n-gram vn
, which is n 2 {1, 2}
to represent psychological features, su
terns and the behavioral tendency o
polarity, emotion, and social interacti
full BDPLF, there are five categories:
• Age and Gender: Sit et al. [2
effects on BD, indicating that wom
likely to have Bipolar Disorder
than men. We make use of the ag
proposed by Sap et al. [19], whic
social media.
• Mood Polarity Features: Ow
BD patients experience rapid mo
analysis is firstly adapted to obt
larity portrayed by each user’s t
the sentiment of tweets, the onlin
used, based on Go et al.’s work [
the contents of tweets into thre
positive, negative, and neutral.
those three categories into five
positive ratio, negative ratio, po
combo, and flips ratio.
• Emotional Scores: Beyond th
tion detection tool proposed by
employed to classify the tweets in
gories: joy, surprise, anticipation,
anger, and fear. The emotion cla
further transformed into emotio
esei,
(k)
⌧,↵
=
ei,
(k)
⌧,↵
ecount
진단 받기 1년 전 부터의 트윗을 대조군과 비교 분석
개별 feature만으로도 위험군 분류에 높은 precision을 보였음](https://image.slidesharecdn.com/digitalhealthcare-intheperspectiveofmentaldisorder-180420075555/85/slide-57-320.jpg)
![ORIGINAL ARTICLE
Watson for Oncology and breast cancer treatment
recommendations: agreement with an expert
multidisciplinary tumor board
S. P. Somashekhar1*, M.-J. Sepu´lveda2
, S. Puglielli3
, A. D. Norden3
, E. H. Shortliffe4
, C. Rohit Kumar1
,
A. Rauthan1
, N. Arun Kumar1
, P. Patil1
, K. Rhee3
& Y. Ramya1
1
Manipal Comprehensive Cancer Centre, Manipal Hospital, Bangalore, India; 2
IBM Research (Retired), Yorktown Heights; 3
Watson Health, IBM Corporation,
Cambridge; 4
Department of Surgical Oncology, College of Health Solutions, Arizona State University, Phoenix, USA
*Correspondence to: Prof. Sampige Prasannakumar Somashekhar, Manipal Comprehensive Cancer Centre, Manipal Hospital, Old Airport Road, Bangalore 560017, Karnataka,
India. Tel: þ91-9845712012; Fax: þ91-80-2502-3759; E-mail: somashekhar.sp@manipalhospitals.com
Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug
approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to
help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment
recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer.
Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the
Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in
2016. A blinded second review was carried out by the center’s tumor board in 2016 for all cases in which there was not
agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered
concordant if the tumor board recommendations were designated ‘recommended’ or ‘for consideration’ by WFO.
Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases.
Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III
disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P 0.02;
P < 0.001) in all age groups compared with patients <45 years of age, except for the age group 55–64 years. Receptor status
was not found to affect concordance.
Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases
examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not.
This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment
decision making, especially at centers where expert breast cancer resources are limited.
Key words: Watson for Oncology, artificial intelligence, cognitive clinical decision-support systems, breast cancer,
concordance, multidisciplinary tumor board
Introduction
Oncologists who treat breast cancer are challenged by a large and
rapidly expanding knowledge base [1, 2]. As of October 2017, for
example, there were 69 FDA-approved drugs for the treatment of
breast cancer, not including combination treatment regimens
[3]. The growth of massive genetic and clinical databases, along
with computing systems to exploit them, will accelerate the speed
of breast cancer treatment advances and shorten the cycle time
for changes to breast cancer treatment guidelines [4, 5]. In add-
ition, these information management challenges in cancer care
are occurring in a practice environment where there is little time
available for tracking and accessing relevant information at the
point of care [6]. For example, a study that surveyed 1117 oncolo-
gists reported that on average 4.6 h per week were spent keeping
VC The Author(s) 2018. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
All rights reserved. For permissions, please email: journals.permissions@oup.com.
Annals of Oncology 29: 418–423, 2018
doi:10.1093/annonc/mdx781
Published online 9 January 2018
Downloaded from https://academic.oup.com/annonc/article-abstract/29/2/418/4781689
by guest](https://image.slidesharecdn.com/digitalhealthcare-intheperspectiveofmentaldisorder-180420075555/85/slide-72-320.jpg)
![Copyright 2016 American Medical Association. All rights reserved.
Development and Validation of a Deep Learning Algorithm
for Detection of Diabetic Retinopathy
in Retinal Fundus Photographs
Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD;
Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB;
Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD
IMPORTANCE Deep learning is a family of computational methods that allow an algorithm to
program itself by learning from a large set of examples that demonstrate the desired
behavior, removing the need to specify rules explicitly. Application of these methods to
medical imaging requires further assessment and validation.
OBJECTIVE To apply deep learning to create an algorithm for automated detection of diabetic
retinopathy and diabetic macular edema in retinal fundus photographs.
DESIGN AND SETTING A specific type of neural network optimized for image classification
called a deep convolutional neural network was trained using a retrospective development
data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy,
diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists
and ophthalmology senior residents between May and December 2015. The resultant
algorithm was validated in January and February 2016 using 2 separate data sets, both
graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.
EXPOSURE Deep learning–trained algorithm.
MAIN OUTCOMES AND MEASURES The sensitivity and specificity of the algorithm for detecting
referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy,
referable diabetic macular edema, or both, were generated based on the reference standard
of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2
operating points selected from the development set, one selected for high specificity and
another for high sensitivity.
RESULTS TheEyePACS-1datasetconsistedof9963imagesfrom4997patients(meanage,54.4
years;62.2%women;prevalenceofRDR,683/8878fullygradableimages[7.8%]);the
Messidor-2datasethad1748imagesfrom874patients(meanage,57.6years;42.6%women;
prevalenceofRDR,254/1745fullygradableimages[14.6%]).FordetectingRDR,thealgorithm
hadanareaunderthereceiveroperatingcurveof0.991(95%CI,0.988-0.993)forEyePACS-1and
0.990(95%CI,0.986-0.995)forMessidor-2.Usingthefirstoperatingcutpointwithhigh
specificity,forEyePACS-1,thesensitivitywas90.3%(95%CI,87.5%-92.7%)andthespecificity
was98.1%(95%CI,97.8%-98.5%).ForMessidor-2,thesensitivitywas87.0%(95%CI,81.1%-
91.0%)andthespecificitywas98.5%(95%CI,97.7%-99.1%).Usingasecondoperatingpoint
withhighsensitivityinthedevelopmentset,forEyePACS-1thesensitivitywas97.5%and
specificitywas93.4%andforMessidor-2thesensitivitywas96.1%andspecificitywas93.9%.
CONCLUSIONS AND RELEVANCE In this evaluation of retinal fundus photographs from adults
with diabetes, an algorithm based on deep machine learning had high sensitivity and
specificity for detecting referable diabetic retinopathy. Further research is necessary to
determine the feasibility of applying this algorithm in the clinical setting and to determine
whether use of the algorithm could lead to improved care and outcomes compared with
current ophthalmologic assessment.
JAMA. doi:10.1001/jama.2016.17216
Published online November 29, 2016.
Editorial
Supplemental content
Author Affiliations: Google Inc,
Mountain View, California (Gulshan,
Peng, Coram, Stumpe, Wu,
Narayanaswamy, Venugopalan,
Widner, Madams, Nelson, Webster);
Department of Computer Science,
University of Texas, Austin
(Venugopalan); EyePACS LLC,
San Jose, California (Cuadros); School
of Optometry, Vision Science
Graduate Group, University of
California, Berkeley (Cuadros);
Aravind Medical Research
Foundation, Aravind Eye Care
System, Madurai, India (Kim); Shri
Bhagwan Mahavir Vitreoretinal
Services, Sankara Nethralaya,
Chennai, Tamil Nadu, India (Raman);
Verily Life Sciences, Mountain View,
California (Mega); Cardiovascular
Division, Department of Medicine,
Brigham and Women’s Hospital and
Harvard Medical School, Boston,
Massachusetts (Mega).
Corresponding Author: Lily Peng,
MD, PhD, Google Research, 1600
Amphitheatre Way, Mountain View,
CA 94043 (lhpeng@google.com).
Research
JAMA | Original Investigation | INNOVATIONS IN HEALTH CARE DELIVERY
(Reprinted) E1
Copyright 2016 American Medical Association. All rights reserved.](https://image.slidesharecdn.com/digitalhealthcare-intheperspectiveofmentaldisorder-180420075555/85/slide-81-320.jpg)
![Assisting Pathologists in Detecting
Cancer with Deep Learning
6
Input & Validation Test
model size FROC @8FP AUC FROC @8FP AUC
40X 98.1 100 99.0 87.3 (83.2, 91.1) 91.1 (87.2, 94.5) 96.7 (92.6, 99.6)
40X-pretrained 99.3 100 100 85.5 (81.0, 89.5) 91.1 (86.8, 94.6) 97.5 (93.8, 99.8)
40X-small 99.3 100 100 86.4 (82.2, 90.4) 92.4 (88.8, 95.7) 97.1 (93.2, 99.8)
ensemble-of-3 - - - 88.5 (84.3, 92.2) 92.4 (88.7, 95.6) 97.7 (93.0, 100)
20X-small 94.7 100 99.6 85.5 (81.0, 89.7) 91.1 (86.9, 94.8) 98.6 (96.7, 100)
10X-small 88.7 97.2 97.7 79.3 (74.2, 84.1) 84.9 (80.0, 89.4) 96.5 (91.9, 99.7)
40X+20X-small 94.9 98.6 99.0 85.9 (81.6, 89.9) 92.9 (89.3, 96.1) 97.0 (93.1, 99.9)
40X+10X-small 93.8 98.6 100 82.2 (77.0, 86.7) 87.6 (83.2, 91.7) 98.6 (96.2, 99.9)
Pathologist [1] - - - 73.3* 73.3* 96.6
Camelyon16 winner [1, 23] - - - 80.7 82.7 99.4
Table 1. Results on Camelyon16 dataset (95% confidence intervals, CI). Bold indicates
results within the CI of the best model. “Small” models contain 300K parameters per
Inception tower instead of 20M. -: not reported. *A pathologist achieved this sensitivity
(with no FP) using 30 hours.
to 10 20% variance), and can confound evaluation of model improvements
by grouping multiple nearby tumors as one. By contrast, our non-maxima sup-
pression approach is relatively insensitive to r between 4 and 6, although less
accurate models benefited from tuning r using the validation set (e.g., 8). Fi-
The FROC evaluates tumor detection and localization
The FROC is defined as the sensitivity at 0.25,0.5,1,2,4,8 average FPs per tumor-negative slide.
Yun Liu et al. Detecting Cancer Metastases on Gigapixel Pathology Images (2017)
Sensitivity at 8 false positives per image](https://image.slidesharecdn.com/digitalhealthcare-intheperspectiveofmentaldisorder-180420075555/85/slide-92-320.jpg)
![RESEARCH ARTICLE
A pilot study to determine the feasibility of
enhancing cognitive abilities in children with
sensory processing dysfunction
Joaquin A. Anguera1,2☯
*, Anne N. Brandes-Aitken1☯
, Ashley D. Antovich1
, Camarin
E. Rolle1
, Shivani S. Desai1
, Elysa J. Marco1,2,3
1 Department of Neurology, University of California, San Francisco, United States of America, 2 Department
of Psychiatry, University of California, San Francisco, United States of America, 3 Department of Pediatrics,
University of California, San Francisco, United States of America
☯ These authors contributed equally to this work.
* joaquin.anguera@ucsf.edu
Abstract
Children with Sensory Processing Dysfunction (SPD) experience incoming information in
atypical, distracting ways. Qualitative challenges with attention have been reported in these
children, but such difficulties have not been quantified using either behavioral or functional
neuroimaging methods. Furthermore, the efficacy of evidence-based cognitive control inter-
ventions aimed at enhancing attention in this group has not been tested. Here we present
work aimed at characterizing and enhancing attentional abilities for children with SPD. A
sample of 38 SPD and 25 typically developing children were tested on behavioral, neural,
and parental measures of attention before and after a 4-week iPad-based at-home cognitive
remediation program. At baseline, 54% of children with SPD met or exceeded criteria on a
parent report measure for inattention/hyperactivity. Significant deficits involving sustained
attention, selective attention and goal management were observed only in the subset of
SPD children with parent-reported inattention. This subset of children also showed reduced
midline frontal theta activity, an electroencephalographic measure of attention. Following
the cognitive intervention, only the SPD children with inattention/hyperactivity showed both
improvements in midline frontal theta activity and on a parental report of inattention. Notably,
33% of these individuals no longer met the clinical cut-off for inattention, with the parent-
reported improvements persisting for 9 months. These findings support the benefit of a
targeted attention intervention for a subset of children with SPD, while simultaneously
highlighting the importance of having a multifaceted assessment for individuals with neuro-
developmental conditions to optimally personalize treatment.
Introduction
Five percent of all children suffer from Sensory Processing Dysfunction (SPD)[1], with these
individuals exhibiting exaggerated aversive, withdrawal, or seeking behaviors associated with
sensory inputs [2]. These sensory processing differences can have significant and lifelong con-
sequences for learning and social abilities, and are often shared by children who meet
PLOS ONE | https://doi.org/10.1371/journal.pone.0172616 April 5, 2017 1 / 19
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Anguera JA, Brandes-Aitken AN, Antovich
AD, Rolle CE, Desai SS, Marco EJ (2017) A pilot
study to determine the feasibility of enhancing
cognitive abilities in children with sensory
processing dysfunction. PLoS ONE 12(4):
e0172616. https://doi.org/10.1371/journal.
pone.0172616
Editor: Jacobus P. van Wouwe, TNO,
NETHERLANDS
Received: October 5, 2016
Accepted: February 1, 2017
Published: April 5, 2017
Copyright: © 2017 Anguera et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This work was supported by the
Mickelson-Brody Family Foundation, the Wallace
Research Foundation, the James Gates Family
Foundation, the Kawaja-Holcombe Family
Foundation (EJM), and the SNAP 2015 Crowd
funding effort.
•감각처리장애(SPD)를 가진 소아 환자 중 ADHD를 가진 20명에 대해서 실험
•4주 동안 (주당 5일, 25분)Project EVO 게임을 하게 한 결과,
•20명 중 7명이 큰 개선을 보여서 더 이상 ADHD의 범주에 들지 않게 됨
•사용 후 적어도 9개월 동안 효과가 지속되었음
Fig 4. Transfer effect on behavioral and parent report measures. Pre and post (A) response time (B) and resp
revealing within group change. Error bars indicate standard error of the mean. Within group main effects of session
= p .05, ** =.p .01. Sun symbols indicate statistically significant instances where SPD+IA post-training performa
TDC group prior to training. (C) Vanderbilt parent report inattention change bar plot (calculated by pre-post margina
significant group x session interaction. Error bars indicate standard error of the mean. All group x session interactio
stars (* = p .05, ** =.p .01) on bar graph.
https://doi.org/10.1371/journal.pone.0172616.g004
PLOS ONE | https://doi.org/10.1371/journal.pone.0172616 April 5, 2017](https://image.slidesharecdn.com/digitalhealthcare-intheperspectiveofmentaldisorder-180420075555/85/slide-161-320.jpg)
Uploaded byYoon Sup Choi
의료의 미래, 디지털 헬스케어: 정신의학을 중심으로
1) The document discusses the concept of a "digital phenotype", which refers to aspects of a person's interactions with technology that can provide diagnostic or prognostic insights into their health conditions. 2) Previous research has found correlations between depressive symptom severity and certain location-based smartphone sensor data, such as increased location variance and disrupted circadian rhythms. 3) This study replicates previous findings using GPS smartphone sensor data collected from 48 college students over 10 weeks, finding significant correlations between depressive symptoms and location variance, entropy, and circadian movement patterns. The relationships were stronger when analyzing weekend versus weekday data.
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의료의 미래, 디지털 헬스케어: 정신의학을 중심으로
- 1. Professor, SAHIST, SungkyunkwanUniversity Director, Digital Healthcare Institute Yoon Sup Choi, Ph.D. 의료의 미래, 디지털 헬스케어 : 정신의학을 중심으로
- 2. “It's in Apple'sDNA that technology alone is not enough. It's technology married with liberal arts.”
- 3. The Convergence ofIT, BT and Medicine
- 6. Inevitable Tsunami ofChange
- 7.
- 8. Vinod Khosla Founder, 1stCEO of Sun Microsystems Partner of KPCB, CEO of KhoslaVentures LegendaryVenture Capitalist in SiliconValley
- 9. “Technology will replace80% of doctors”
- 10. https://www.youtube.com/watch?time_continue=70&v=2HMPRXstSvQ “영상의학과 전문의를 양성하는것을 당장 그만둬야 한다. 5년 안에 딥러닝이 영상의학과 전문의를 능가할 것은 자명하다.” Hinton on Radiology
- 12. 헬스케어넓은 의미의 건강관리에는 해당되지만, 디지털 기술이 적용되지 않고, 전문 의료 영역도 아닌 것 예) 운동, 영양, 수면 디지털 헬스케어 건강 관리 중에 디지털 기술이 사용되는 것 예) 사물인터넷, 인공지능, 3D 프린터 모바일 헬스케어 디지털 헬스케어 중 모바일 기술이 사용되는 것 예) 스마트폰, 사물인터넷, SNS 개인 유전정보분석 예) 암유전체, 질병위험도, 보인자, 약물 민감도 예) 웰니스, 조상 분석 헬스케어 관련 분야 구성도 (ver 0.3) 의료 질병 예방, 치료, 처방, 관리 등 전문 의료 영역 원격의료 원격진료
- 13. What is mostimportant factor in digital medicine?
- 14. “Data! Data! Data!”he cried.“I can’t make bricks without clay!” - Sherlock Holmes,“The Adventure of the Copper Beeches”
- 16. 새로운 데이터가 새로운 방식으로 새로운주체에 의해 측정, 저장, 통합, 분석된다. 데이터의 종류 데이터의 질적/양적 측면 웨어러블 기기 스마트폰 유전 정보 분석 인공지능 SNS 사용자/환자 대중
- 17. Digital Healthcare IndustryLandscape Data Measurement Data Integration Data Interpretation Treatment Smartphone Gadget/Apps DNA Artificial Intelligence 2nd Opinion Wearables / IoT (ver. 3) EMR/EHR 3D Printer Counseling Data Platform Accelerator/early-VC Telemedicine Device On Demand (O2O) VR Digital Healthcare Institute Diretor, Yoon Sup Choi, Ph.D. yoonsup.choi@gmail.com
- 18. Data Measurement DataIntegration Data Interpretation Treatment Smartphone Gadget/Apps DNA Artificial Intelligence 2nd Opinion Device On Demand (O2O) Wearables / IoT Digital Healthcare Institute Diretor, Yoon Sup Choi, Ph.D. yoonsup.choi@gmail.com EMR/EHR 3D Printer Counseling Data Platform Accelerator/early-VC VR Telemedicine Digital Healthcare Industry Landscape (ver. 3)
- 19. Digital Phenotype 당신의 스마트폰은당신이 우울한지 알고 있다
- 20. Smartphone: the originof healthcare innovation
- 21. 2013? The election ofPope Benedict The Election of Pope Francis
- 22. The Election ofPope Francis The Election of Pope Benedict
- 23.
- 27. • 아이폰의 센서로측정한 자신의 의료/건강 데이터를 플랫폼에 공유 가능 • 가속도계, 마이크, 자이로스코프, GPS 센서 등을 이용 • 걸음, 운동량, 기억력, 목소리 떨림 등등 • 기존의 의학연구의 문제를 해결: 충분한 의료 데이터의 확보 • 연구 참여자 등록에 물리적, 시간적 장벽을 제거 (1번/3개월 ➞ 1번/1초) • 대중의 의료 연구 참여 장려: 연구 참여자의 수 증가 • 발표 후 24시간 내에 수만명의 연구 참여자들이 지원 • 사용자 본인의 동의 하에 진행 Research Kit
- 28. •초기 버전으로, 5가지질환에 대한 앱 5개를 소개 ResearchKit
- 29.
- 30.
- 31.
- 32. http://www.roche.com/media/store/roche_stories/roche-stories-2015-08-10.htm pRED app totrack Parkinson’s symptoms in drug trial
- 33. the manifestations ofdisease by providing a more comprehensive and nuanced view of the experience of illness. Through the lens of the digital phenotype, an individual’s interaction The digital phenotype Sachin H Jain, Brian W Powers, Jared B Hawkins & John S Brownstein In the coming years, patient phenotypes captured to enhance health and wellness will extend to human interactions with digital technology. In 1982, the evolutionary biologist Richard Dawkins introduced the concept of the “extended phenotype”1, the idea that pheno- types should not be limited just to biological processes, such as protein biosynthesis or tissue growth, but extended to include all effects that a gene has on its environment inside or outside ofthebodyoftheindividualorganism.Dawkins stressed that many delineations of phenotypes are arbitrary. Animals and humans can modify their environments, and these modifications andassociatedbehaviorsareexpressionsofone’s genome and, thus, part of their extended phe- notype. In the animal kingdom, he cites damn buildingbybeaversasanexampleofthebeaver’s extended phenotype1. Aspersonaltechnologybecomesincreasingly embedded in human lives, we think there is an important extension of Dawkins’s theory—the notion of a ‘digital phenotype’. Can aspects of ourinterfacewithtechnologybesomehowdiag- nosticand/orprognosticforcertainconditions? Can one’s clinical data be linked and analyzed together with online activity and behavior data to create a unified, nuanced view of human dis- ease?Here,wedescribetheconceptofthedigital phenotype. Although several disparate studies have touched on this notion, the framework for medicine has yet to be described. We attempt to define digital phenotype and further describe the opportunities and challenges in incorporat- ing these data into healthcare. Jan. 2013 0.000 0.002 0.004 Density 0.006 July 2013 Jan. 2014 July 2014 User 1 User 2 User 3 User 4 User 5 User 6 User 7 Date Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions (probability density functions) are shown for seven individual users over a two-year period. Density on the y axis highlights periods of relative activity for each user. A representative tweet from each user is shown as an example. npg©2015NatureAmerica,Inc.Allrightsreserved. http://www.nature.com/nbt/journal/v33/n5/full/nbt.3223.html
- 35.
- 36.
- 37.
- 38.
- 39.
- 40.
- 41. Digital Phenotype: Your smartphoneknows if you are depressed Ginger.io
- 42. Ginger.io •문자를 얼마나 자주하는지 •통화를 얼마나 오래하는지 •누구와 통화를 하는지 •얼마나 거리를 많이 이동했는지 •얼마나 많이 움직였는지 • UCSF, McLean Hospital: 정신질환 연구 • Novant Health: 당뇨병, 산후 우울증 연구 • UCSF, Duke: 수술 후 회복 모니터링
- 43. Digital Phenotype: Your smartphoneknows if you are depressed J Med Internet Res. 2015 Jul 15;17(7):e175. The correlation analysis between the features and the PHQ-9 scores revealed that 6 of the 10 features were significantly correlated to the scores: • strong correlation: circadian movement, normalized entropy, location variance • correlation: phone usage features, usage duration and usage frequency
- 44. Digital Phenotype: Your smartphoneknows if you are depressed J Med Internet Res. 2015 Jul 15;17(7):e175. Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the ones with (red). (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay;TT, transition time;TD, total distance; CM, circadian movement; NC, number of clusters; UF, usage frequency; UD, usage duration). Figure 4. Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the ones with (red). Feature values are scaled between 0 and 1 for easier comparison. Boxes extend between 25th and 75th percentiles, and whiskers show the range. Horizontal solid lines inside the boxes are medians. One, two, and three asterisks show significant differences at P<.05, P<.01, and P<.001 levels, respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance; CM, circadian movement; NC, number of clusters; UF, usage frequency; UD, usage duration). Figure 5. Coefficients of correlation between location features. One, two, and three asterisks indicate significant correlation levels at P<.05, P<.01, and P<.001, respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance; CM, circadian movement; NC, number of clusters). Saeb et alJOURNAL OF MEDICAL INTERNET RESEARCH the variability of the time the participant spent at the location clusters what extent the participants’ sequence of locations followed a circadian rhythm. home stay
- 45. Submitted 23 June2016 Accepted 7 September 2016 Published 29 September 2016 Corresponding author David C. Mohr, d-mohr@northwestern.edu Academic editor Anthony Jorm Additional Information and Declarations can be found on page 12 DOI 10.7717/peerj.2537 Copyright 2016 Saeb et al. Distributed under Creative Commons CC-BY 4.0 OPEN ACCESS The relationship between mobile phone location sensor data and depressive symptom severity Sohrab Saeb1,2 , Emily G. Lattie1 , Stephen M. Schueller1 , Konrad P. Kording2 and David C. Mohr1 1 Department of Preventive Medicine, Northwestern University, Chicago, IL, United States 2 Rehabilitation Institute of Chicago, Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States ABSTRACT Background. Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate andextendpreviousworkusinggeographiclocation(GPS)sensorstoidentifydepressive symptom severity. Methods. We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate depressive symptom severity at baseline and end-of-study. GPS featureswerecalculatedovertheentirestudy,forweekdaysandweekends,andin2-week blocks. Results. The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r’s ranging from 0.43 to 0.46, p-values < .05). We also found that these relationships were stronger when GPS features were calculatedfromweekend,comparedtoweekday,data.Althoughthecorrelationbetween baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features. Discussion. Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of depressive symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict depressive symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression. Subjects Bioinformatics, Psychiatry and Psychology, Public Health, Computational Science Keywords Mobile phone, Depression, Depressive symptoms, Geographic locations, Students INTRODUCTION Depression is common and debilitating, taking an enormous toll in terms of cost, morbidity, and mortality (Ferrari et al., 2013; Greenberg et al., 2015). The 12-month prevalence of major depressive disorder among adults in the US is 6.9% (Kessler et al., 2005), and an additional 2–5% have subsyndromal symptoms that warrant treatment Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537
- 46. The relationship betweenmobile phone location sensor data and depressive symptom severity Linear correlation coefficients (r) between individual 10-week features and PHQ-9 scores, and their 95% confidence intervals. Features indicated with stars (∗) are replicated from our previous study (Saeb et al., 2015a.). Bold values indicate significant correlations. Table 2 Linear correlation coefficients (r) between individual 10-week features and PHQ-9 scores, and their 95% confidence intervals. Features indicated with stars (⇤) are replicated from our previous study (Saeb et al., 2015a.). Bold values indicate significant correlations. Feature Baseline (n = 46) Follow-up (n = 38) Change (n = 38) Location variance⇤ 0.29 ± 0.008 0.43 ± 0.007 0.34 ± 0.008 Circadian movement⇤ 0.34 ± 0.006 0.48 ± 0.006 0.33 ± 0.009 Speed mean 0.03 ± 0.007 0.06 ± 0.005 0.04 ± 0.008 Speed variance 0.07 ± 0.007 0.06 ± 0.005 0.06 ±0.007 Total distance⇤ 0.23 ± 0.004 0.18 ± 0.006 0.03 ± 0.006 Number of clusters⇤ 0.38 ± 0.005 0.44 ± 0.004 0.24 ± 0.007 Entropy⇤ 0.31 ± 0.007 0.46 ± 0.005 0.28 ± 0.008 Normalized entropy⇤ 0.26 ± 0.007 0.44 ± 0.005 0.30 ± 0.009 Raw entropy 0.17 ± 0.009 0.22 ± 0.008 0.15 ± 0.010 Home stay⇤ 0.22 ± 0.008 0.43 ± 0.005 0.30 ± 0.009 Transition time⇤ 0.30 ± 0.006 0.32 ± 0.005 0.12 ± 0.009 Data analysis We evaluated the relationship between each set of features (10-week and 2-week, each for all days, weekends, or weekdays) and depressive symptoms severity as measured by the PHQ-9. We used linear correlation coefficient (r) and considered p < 0.05 as the significance level. In order to reduce the possibility that results were generated by chance, we created 1,000 bootstrap subsamples (Efron & Tibshirani, 1993) to estimate these correlation coefficientsSaeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537
- 47. Table 3 Linearcorrelation coefficients (r) between individual weekend and weekday features and PHQ-9 scores, and their 95% confidence in- tervals. Bold values indicate significant correlations (see ‘Data Analysis’). Feature Weekday Weekend Baseline (n = 46) Follow-up (n = 38) Change (n = 38) Baseline (n = 46) Follow-up (n = 38) Change (n = 38) Location variance 0.15 ± 0.008 0.20 ± 0.008 0.22 ± 0.009 0.31 ± 0.008 0.47 ±0.007 0.39 ± 0.008 Circadian movement 0.22 ± 0.007 0.28 ± 0.008 0.25 ± 0.009 0.35 ± 0.007 0.51 ±0.006 0.36 ± 0.008 Speed mean 0.00 ± 0.008 0.06 ± 0.005 0.03 ± 0.008 0.13 ± 0.005 0.06 ± 0.006 0.05 ± 0.009 Speed variance 0.05 ± 0.008 0.07 ± 0.005 0.02 ± 0.007 0.13 ± 0.004 0.05 ± 0.006 0.10 ± 0.008 Total distance 0.20 ± 0.004 0.15 ± 0.005 0.01 ± 0.006 0.25 ± 0.004 0.20 ± 0.005 0.03 ± 0.006 Number of clusters 0.19 ± 0.006 0.25 ± 0.005 0.14 ± 0.008 0.34 ± 0.006 0.46 ±0.004 0.32 ± 0.007 Entropy 0.21 ± 0.007 0.34 ± 0.006 0.20 ± 0.009 0.30 ± 0.008 0.55 ±0.004 0.38 ± 0.008 Normalized entropy 0.21 ± 0.008 0.39 ± 0.006 0.24 ± 0.009 0.28 ± 0.008 0.54 ± 0.004 0.41 ± 0.009 Raw entropy 0.05 ± 0.008 0.04 ± 0.008 0.01 ± 0.010 0.04 ± 0.008 0.01 ± 0.008 0.03 ± 0.009 Home stay 0.19 ± 0.008 0.37 ± 0.006 0.23 ± 0.009 0.23 ± 0.007 0.50 ± 0.004 0.35 ± 0.008 Transition time 0.27 ± 0.006 0.29 ± 0.006 0.14 ± 0.010 0.36 ± 0.006 0.32 ± 0.008 0.06 ± 0.009 only normalized entropy was significantly related to the scores as a weekday feature. The magnitude of the relationship between weekend features and PHQ-9 scores was larger than the magnitude of the relationship between 10-week features and PHQ-9 scores. However, given the small sample size, we were not adequately powered to test if these differences were significant. 2-week features Finally, we examined how 2-week GPS features obtained at different times during the study Linear correlation coefficients (r) between individual weekend and weekday features and PHQ-9 scores, and their 95% confidence intervals. Bold values indicate significant correlations.All of those 10-week features that were significantly related to PHQ-9 scores (seeTable 2) were also significant when calculated from weekends, whereas only normalized entropy was significantly related to the scores as a weekday feature Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537 The relationship between mobile phone location sensor data and depressive symptom severity
- 48. Saeb et al.(2016), PeerJ, DOI 10.7717/peerj.2537 Mean temporal correlations between 2-week location features, calculated at different time points during the study, and baseline and follow-up PHQ-9 scores. The relationship between mobile phone location sensor data and depressive symptom severity
- 49. Reece & Danforth,“Instagram photos reveal predictive markers of depression” (2016) higher Hue (bluer) lower Saturation (grayer) lower Brightness (darker)
- 50. Digital Phenotype: Your Instagramknows if you are depressed Rao (MVR) (24) . Results Both Alldata and Prediagnosis models were decisively superior to a null model . Alldata predictors were significant with 99% probability.57.5;(KAll = 1 K 49.8) Pre = 1 7 Prediagnosis and Alldata confidence levels were largely identical, with two exceptions: Prediagnosis Brightness decreased to 90% confidence, and Prediagnosis posting frequency dropped to 30% confidence, suggesting a null predictive value in the latter case. Increased hue, along with decreased brightness and saturation, predicted depression. This means that photos posted by depressed individuals tended to be bluer, darker, and grayer (see Fig. 2). The more comments Instagram posts received, the more likely they were posted by depressed participants, but the opposite was true for likes received. In the Alldata model, higher posting frequency was also associated with depression. Depressed participants were more likely to post photos with faces, but had a lower average face count per photograph than healthy participants. Finally, depressed participants were less likely to apply Instagram filters to their posted photos. Fig. 2. Magnitude and direction of regression coefficients in Alldata (N=24,713) and Prediagnosis (N=18,513) models. Xaxis values represent the adjustment in odds of an observation belonging to depressed individuals, per Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) Fig. 1. Comparison of HSV values. Right photograph has higher Hue (bluer), lower Saturation (grayer), and lower Brightness (darker) than left photograph. Instagram photos posted by depressed individuals had HSV values shifted towards those in the right photograph, compared with photos posted by healthy individuals. Units of observation In determining the best time span for this analysis, we encountered a difficult question: When and for how long does depression occur? A diagnosis of depression does not indicate the persistence of a depressive state for every moment of every day, and to conduct analysis using an individual’s entire posting history as a single unit of observation is therefore rather specious. At the other extreme, to take each individual photograph as units of observation runs the risk of being too granular. DeChoudhury et al. (5) looked at all of a given user’s posts in a single day, and aggregated those data into perperson, perday units of observation. We adopted this precedent of “userdays” as a unit of analysis . 5 Statistical framework We used Bayesian logistic regression with uninformative priors to determine the strength of individual predictors. Two separate models were trained. The Alldata model used all collected data to address Hypothesis 1. The Prediagnosis model used all data collected from higher Hue (bluer) lower Saturation (grayer) lower Brightness (darker)
- 51. Digital Phenotype: Your Instagramknows if you are depressed Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) . In particular, depressedχ2 07.84, p .17e 64;( All = 9 = 9 − 1 13.80, p .87e 44)χ2Pre = 8 = 2 − 1 participants were less likely than healthy participants to use any filters at all. When depressed participants did employ filters, they most disproportionately favored the “Inkwell” filter, which converts color photographs to blackandwhite images. Conversely, healthy participants most disproportionately favored the Valencia filter, which lightens the tint of photos. Examples of filtered photographs are provided in SI Appendix VIII. Fig. 3. Instagram filter usage among depressed and healthy participants. Bars indicate difference between observed and expected usage frequencies, based on a Chisquared analysis of independence. Blue bars indicate disproportionate use of a filter by depressed compared to healthy participants, orange bars indicate the reverse.
- 52. Digital Phenotype: Your Instagramknows if you are depressed Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) VIII. Instagram filter examples Fig. S8. Examples of Inkwell and Valencia Instagram filters. Inkwell converts color photos to blackandwhite, Valencia lightens tint. Depressed participants most favored Inkwell compared to healthy participants, Healthy participants
- 53. the manifestations ofdisease by providing a more comprehensive and nuanced view of the experience of illness. Through the lens of the digital phenotype, an individual’s interaction The digital phenotype Sachin H Jain, Brian W Powers, Jared B Hawkins & John S Brownstein In the coming years, patient phenotypes captured to enhance health and wellness will extend to human interactions with digital technology. In 1982, the evolutionary biologist Richard Dawkins introduced the concept of the “extended phenotype”1, the idea that pheno- types should not be limited just to biological processes, such as protein biosynthesis or tissue growth, but extended to include all effects that a gene has on its environment inside or outside ofthebodyoftheindividualorganism.Dawkins stressed that many delineations of phenotypes are arbitrary. Animals and humans can modify their environments, and these modifications andassociatedbehaviorsareexpressionsofone’s genome and, thus, part of their extended phe- notype. In the animal kingdom, he cites damn buildingbybeaversasanexampleofthebeaver’s extended phenotype1. Aspersonaltechnologybecomesincreasingly embedded in human lives, we think there is an important extension of Dawkins’s theory—the notion of a ‘digital phenotype’. Can aspects of ourinterfacewithtechnologybesomehowdiag- nosticand/orprognosticforcertainconditions? Can one’s clinical data be linked and analyzed together with online activity and behavior data to create a unified, nuanced view of human dis- ease?Here,wedescribetheconceptofthedigital phenotype. Although several disparate studies have touched on this notion, the framework for medicine has yet to be described. We attempt to define digital phenotype and further describe the opportunities and challenges in incorporat- ing these data into healthcare. Jan. 2013 0.000 0.002 0.004 Density 0.006 July 2013 Jan. 2014 July 2014 User 1 User 2 User 3 User 4 User 5 User 6 User 7 Date Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions (probability density functions) are shown for seven individual users over a two-year period. Density on the y axis highlights periods of relative activity for each user. A representative tweet from each user is shown as an example. npg©2015NatureAmerica,Inc.Allrightsreserved. http://www.nature.com/nbt/journal/v33/n5/full/nbt.3223.html
- 54. ers, Jared BHawkins & John S Brownstein phenotypes captured to enhance health and wellness will extend to human interactions with st Richard pt of the hat pheno- biological sis or tissue effects that or outside m.Dawkins phenotypes can modify difications onsofone’s ended phe- cites damn thebeaver’s ncreasingly there is an heory—the aspects of ehowdiag- Jan. 2013 0.000 0.002 0.004 Density 0.006 July 2013 Jan. 2014 July 2014 User 1 User 2 User 3 User 4 User 5 User 6 User 7 Date Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions (probability density functions) are shown for seven individual users over a two-year period. Density on the y axis highlights periods of relative activity for each user. A representative tweet from each user is Your twitter knows if you cannot sleep Timeline of insomnia-related tweets from representative individuals. Nat. Biotech. 2015
- 55. •트위터 내용과 패턴을바탕으로 양극성 장애 환자와 정상인 구분 •포스팅 패턴, 빈도, 단어의 분석 통한 감정 파악 •음운론 기반의 phonological feature •high energy word: 얼마나 강한 발음/억양의 단어를 사용하는가 •양극성 장애를 진단 받은 406명의 환자 트윗 •진단 받기 1년 전부터의 트윗을 대조군과 비교 •90% 이상의 정확도(precision)으로 구분 가능
- 56. Detection of theProdromal Phase of Bipolar Disorder from Psychological and Phonological Aspects in Social Media Yen-Hao Huang National Tsing Hua University Hsinchu, Taiwan yenhao0218@gmail.com Lin-Hung Wei National Tsing Hua University Hsinchu, Taiwan adeline80916@gmail.com Yi-Shin Chen National Tsing Hua University Hsinchu, Taiwan yishin@gmail.com ABSTRACT Seven out of ten people with bipolar disorder are initially misdiagnosed and thirty percent of individuals with bipolar disorder will commit suicide. Identifying the early phases of the disorder is one of the key components for reducing the full development of the disorder. In this study, we aim at leveraging the data from social media to design predictive models, which utilize the psychological and phonological fea- tures, to determine the onset period of bipolar disorder and provide insights on its prodrome. This study makes these dis- coveries possible by employing a novel data collection process, coined as Time-specific Subconscious Crowdsourcing, which helps collect a reliable dataset that supplements diagnosis information from people suffering from bipolar disorder. Our experimental results demonstrate that the proposed models could greatly contribute to the regular assessments of people with bipolar disorder, which is important in the primary care setting. KEYWORDS Bipolar Disorder Detection, Mental Disorder, Prodromal Phrase, Emotion Analysis, Sentiment Analysis, Phonology, Social Media 1 INTRODUCTION Bipolar disorder (BD) is a common mental illness charac- terized by recurrent episodes of mania/hypomania and de- pression, which is found among all ages, races, ethnic groups and social classes. The regular assessment of people with BD is an important part of its treatment, though it may be very time-consuming [21]. There are many beneficial treat- ments for the patients, particularly for delaying relapses. The identification of early symptoms is significant for allowing early intervention and reducing the multiple adverse conse- quences of a full-blown episode. Despite the importance of the detection of prodromal symptoms, there are very few studies that have actually examined the ability of relatives to detect these symptoms in BD patients. [20] For the purpose of early treatment, the challenge leads to: how to identify the prodrome period of BD. Current studies are thus aimed at detecting prodromes and analyzing the prodromal symptoms of manic recurrence in clinics. With regards to the symptom of social isolation, people are increasingly turning to popular social media, such as Facebook and Twitter, to share their illness experiences or seek advice from others with similar mental health conditions. As the information is being shared in public, people are subconsciously providing rich contents about their states of mind. In this paper, we refer to this sharing and data collection as time-specific subconscious crowdsourcing. In this study, we carefully look at patients who have been diagnosed with BD and who explicitly indicate the diagnosis and time of diagnosis on Twitter. Our goal is to both predict whether BD rises on a given period of time, and to discover the prodromal period for BD. It’s important to clarify that our goal doesn’t seek to offer a diagnosis but rather to make a prediction of which users are likely to be suffering from the BD. The main contributions of our work are: • Introducing the concept of time-specific subconscious crowdsourcing, which can aid in locating the social network behavior data of BD patients with the corre- sponding time of diagnosis. • A BD assessment mechanism that differentiates be- tween prodromal symptoms and acute symptoms. • Introducing the phonological features into the assess- ment mechanism, which allows for the possibility to assess patients through text only. • An automatic recognition approach that detects the possible prodromal period for BD. 2 RELATED WORK Social media resources have been widely utilized by researchers to study mental health issues. The following literature em- phasizes on data collection and feature engineering, including subject recruitment, manual data collection, data collection applications, keyword matching, and combined approaches. The clinical approach for mental disorders and prodrome studies are also discussed in this section. Subject recruitment: Based on customized question- naires and contact with subjects, Park et al. [15] recruited participants for the Center for Epidemiologic Studies Depres- sion scale(CES-D) [17] and provided their Twitter data. By analyzing the information contained in tweets, participants were divided into normal and depressive groups based on their scores on CES-D. An approach like this one requires ex- pensive costs to acquire data and conduct the questionnaire. Manual and automatic data collecting: Moreno et al. [14] collected data via the Facebook profiles of college stu- dents reviewed by two investigators. They aimed at revealing the relationship between demographic factors and depression. Similarly, in our work, we invest on manual efforts to collect and properly annotate our dataset. In addition, there are many applications built on top of social networks that provide free services where users may need to input their credentials arXiv:1712.09183v1[cs.IR]26Dec2017
- 57. Detection of theProdromal Phase of Bipolar Disorder from Psychological and Phonological Aspects in Social Media Yen-Hao Huang National Tsing Hua University Hsinchu, Taiwan yenhao0218@gmail.com Lin-Hung Wei National Tsing Hua University Hsinchu, Taiwan adeline80916@gmail.com Yi-Shin Chen National Tsing Hua University Hsinchu, Taiwan yishin@gmail.com ABSTRACT Seven out of ten people with bipolar disorder are initially misdiagnosed and thirty percent of individuals with bipolar disorder will commit suicide. Identifying the early phases of the disorder is one of the key components for reducing the full development of the disorder. In this study, we aim at leveraging the data from social media to design predictive models, which utilize the psychological and phonological fea- tures, to determine the onset period of bipolar disorder and provide insights on its prodrome. This study makes these dis- coveries possible by employing a novel data collection process, coined as Time-specific Subconscious Crowdsourcing, which helps collect a reliable dataset that supplements diagnosis information from people suffering from bipolar disorder. Our experimental results demonstrate that the proposed models could greatly contribute to the regular assessments of people with bipolar disorder, which is important in the primary care setting. KEYWORDS Bipolar Disorder Detection, Mental Disorder, Prodromal Phrase, Emotion Analysis, Sentiment Analysis, Phonology, Social Media 1 INTRODUCTION Bipolar disorder (BD) is a common mental illness charac- terized by recurrent episodes of mania/hypomania and de- pression, which is found among all ages, races, ethnic groups and social classes. The regular assessment of people with BD is an important part of its treatment, though it may be very time-consuming [21]. There are many beneficial treat- ments for the patients, particularly for delaying relapses. The identification of early symptoms is significant for allowing early intervention and reducing the multiple adverse conse- quences of a full-blown episode. Despite the importance of the detection of prodromal symptoms, there are very few studies that have actually examined the ability of relatives to detect these symptoms in BD patients. [20] For the purpose of early treatment, the challenge leads to: how to identify the prodrome period of BD. Current studies are thus aimed at detecting prodromes and analyzing the prodromal symptoms of manic recurrence in clinics. With regards to the symptom of social isolation, people are increasingly turning to popular social media, such as Facebook and Twitter, to share their illness experiences or seek advice from others with similar mental health conditions. As the information is being shared in public, people are subconsciously providing rich contents about their states of mind. In this paper, we refer to this sharing and data collection as time-specific subconscious crowdsourcing. In this study, we carefully look at patients who have been diagnosed with BD and who explicitly indicate the diagnosis and time of diagnosis on Twitter. Our goal is to both predict whether BD rises on a given period of time, and to discover the prodromal period for BD. It’s important to clarify that our goal doesn’t seek to offer a diagnosis but rather to make a prediction of which users are likely to be suffering from the BD. The main contributions of our work are: • Introducing the concept of time-specific subconscious crowdsourcing, which can aid in locating the social network behavior data of BD patients with the corre- sponding time of diagnosis. • A BD assessment mechanism that differentiates be- tween prodromal symptoms and acute symptoms. • Introducing the phonological features into the assess- ment mechanism, which allows for the possibility to assess patients through text only. • An automatic recognition approach that detects the possible prodromal period for BD. 2 RELATED WORK Social media resources have been widely utilized by researchers to study mental health issues. The following literature em- phasizes on data collection and feature engineering, including subject recruitment, manual data collection, data collection applications, keyword matching, and combined approaches. The clinical approach for mental disorders and prodrome studies are also discussed in this section. Subject recruitment: Based on customized question- naires and contact with subjects, Park et al. [15] recruited participants for the Center for Epidemiologic Studies Depres- sion scale(CES-D) [17] and provided their Twitter data. By analyzing the information contained in tweets, participants were divided into normal and depressive groups based on their scores on CES-D. An approach like this one requires ex- pensive costs to acquire data and conduct the questionnaire. Manual and automatic data collecting: Moreno et al. [14] collected data via the Facebook profiles of college stu- dents reviewed by two investigators. They aimed at revealing the relationship between demographic factors and depression. Similarly, in our work, we invest on manual efforts to collect and properly annotate our dataset. In addition, there are many applications built on top of social networks that provide free services where users may need to input their credentials arXiv:1712.09183v1[cs.IR]26Dec2017 Wordcloud Features(#DIM) 2 mths 3 mths 6 mths 9 mths 12 mths AG(2) 0.475 0.503 0.445 0.434 0.383 Pol(5) 0.911 0.893 0.843 0.836 0.803 Emot(8) 0.893 0.895 0.908 0.917 0.896 Soc(4) 0.941 0.913 0.845 0.834 0.786 LT(1) 0.645 0.589 0.554 0.504 0.513 TRD(1) 0.570 0.638 0.626 0.615 0.654 Phon(8) 0.889 0.880 0.802 0.838 0.821 Table 2: Average Precision of Single Feature Perfor- mance Age and Gender Mood Polarity Features Emotional Score Social Feature Late Tweet Frequency Tweet Rate Difference Phonological Feature Diagnosed time ! " months " = 2 months Figure 1: Illness Period Modeling features are introduced: (1) Word-level features and BD Pattern of Life features. 3.4.1 Word-level Features. With respect to the linguis- tic features for BD, the Character n-gram language fea- tures(CLF) and LIWC metrics are designed to capture it. The CLF utilizes n-grams to measure the comment words or phrases used by users. The tf-idf is utilized in our score- calculating method, the tf is the frequency of an n-gram and the document d of df is defined as each particular twitter user k. The formula for the tf-idf is thus given as: tfidf (k,⌧,↵) vn = freq (k,⌧,↵) vn ⇥ log K 1 + freq (K,⌧,↵) vn (1) The freq (k) vn is the frequency of n-gram vn , which is n 2 {1, 2} to represent psychological features, su terns and the behavioral tendency o polarity, emotion, and social interacti full BDPLF, there are five categories: • Age and Gender: Sit et al. [2 effects on BD, indicating that wom likely to have Bipolar Disorder than men. We make use of the ag proposed by Sap et al. [19], whic social media. • Mood Polarity Features: Ow BD patients experience rapid mo analysis is firstly adapted to obt larity portrayed by each user’s t the sentiment of tweets, the onlin used, based on Go et al.’s work [ the contents of tweets into thre positive, negative, and neutral. those three categories into five positive ratio, negative ratio, po combo, and flips ratio. • Emotional Scores: Beyond th tion detection tool proposed by employed to classify the tweets in gories: joy, surprise, anticipation, anger, and fear. The emotion cla further transformed into emotio esei, (k) ⌧,↵ = ei, (k) ⌧,↵ ecount 진단 받기 1년 전 부터의 트윗을 대조군과 비교 분석 개별 feature만으로도 위험군 분류에 높은 precision을 보였음
- 58. AI for mentalhealth 정신 의학을 위한 인공지능
- 60. No choice butto bring AI into the medicine
- 61. Martin Duggan,“IBM WatsonHealth - Integrated Care & the Evolution to Cognitive Computing”
- 62. •약한 인공 지능(Artificial Narrow Intelligence) • 특정 방면에서 잘하는 인공지능 • 체스, 퀴즈, 메일 필터링, 상품 추천, 자율 운전 •강한 인공 지능 (Artificial General Intelligence) • 모든 방면에서 인간 급의 인공 지능 • 사고, 계획, 문제해결, 추상화, 복잡한 개념 학습 •초 인공 지능 (Artificial Super Intelligence) • 과학기술, 사회적 능력 등 모든 영역에서 인간보다 뛰어난 인공 지능 • “충분히 발달한 과학은 마법과 구분할 수 없다” - 아서 C. 클라크
- 63.
- 64. Jeopardy! 2011년 인간 챔피언두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지
- 65. 600,000 pieces ofmedical evidence 2 million pages of text from 42 medical journals and clinical trials 69 guidelines, 61,540 clinical trials IBM Watson on Medicine Watson learned... + 1,500 lung cancer cases physician notes, lab results and clinical research + 14,700 hours of hands-on training
- 70. Annals of Oncology(2016) 27 (suppl_9): ix179-ix180. 10.1093/annonc/mdw601 Validation study to assess performance of IBM cognitive computing system Watson for oncology with Manipal multidisciplinary tumour board for 1000 consecutive cases: An Indian experience • MMDT(Manipal multidisciplinary tumour board) treatment recommendation and data of 1000 cases of 4 different cancers breast (638), colon (126), rectum (124) and lung (112) which were treated in last 3 years was collected. • Of the treatment recommendations given by MMDT, WFO provided 50% in REC, 28% in FC, 17% in NREC • Nearly 80% of the recommendations were in WFO REC and FC group • 5% of the treatment provided by MMDT was not available with WFO • The degree of concordance varied depending on the type of cancer • WFO-REC was high in Rectum (85%) and least in Lung (17.8%) • high with TNBC (67.9%); HER2 negative (35%) • WFO took a median of 40 sec to capture, analyze and give the treatment. (vs MMDT took the median time of 15 min)
- 71. WFO in ASCO2017 • Early experience with IBM WFO cognitive computing system for lung and colorectal cancer treatment (마니팔 병원) • 지난 3년간: lung cancer(112), colon cancer(126), rectum cancer(124) • lung cancer: localized 88.9%, meta 97.9% • colon cancer: localized 85.5%, meta 76.6% • rectum cancer: localized 96.8%, meta 80.6% Performance of WFO in India 2017 ASCO annual Meeting, J Clin Oncol 35, 2017 (suppl; abstr 8527)
- 72. ORIGINAL ARTICLE Watson forOncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board S. P. Somashekhar1*, M.-J. Sepu´lveda2 , S. Puglielli3 , A. D. Norden3 , E. H. Shortliffe4 , C. Rohit Kumar1 , A. Rauthan1 , N. Arun Kumar1 , P. Patil1 , K. Rhee3 & Y. Ramya1 1 Manipal Comprehensive Cancer Centre, Manipal Hospital, Bangalore, India; 2 IBM Research (Retired), Yorktown Heights; 3 Watson Health, IBM Corporation, Cambridge; 4 Department of Surgical Oncology, College of Health Solutions, Arizona State University, Phoenix, USA *Correspondence to: Prof. Sampige Prasannakumar Somashekhar, Manipal Comprehensive Cancer Centre, Manipal Hospital, Old Airport Road, Bangalore 560017, Karnataka, India. Tel: þ91-9845712012; Fax: þ91-80-2502-3759; E-mail: somashekhar.sp@manipalhospitals.com Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer. Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in 2016. A blinded second review was carried out by the center’s tumor board in 2016 for all cases in which there was not agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered concordant if the tumor board recommendations were designated ‘recommended’ or ‘for consideration’ by WFO. Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases. Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P 0.02; P < 0.001) in all age groups compared with patients <45 years of age, except for the age group 55–64 years. Receptor status was not found to affect concordance. Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not. This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment decision making, especially at centers where expert breast cancer resources are limited. Key words: Watson for Oncology, artificial intelligence, cognitive clinical decision-support systems, breast cancer, concordance, multidisciplinary tumor board Introduction Oncologists who treat breast cancer are challenged by a large and rapidly expanding knowledge base [1, 2]. As of October 2017, for example, there were 69 FDA-approved drugs for the treatment of breast cancer, not including combination treatment regimens [3]. The growth of massive genetic and clinical databases, along with computing systems to exploit them, will accelerate the speed of breast cancer treatment advances and shorten the cycle time for changes to breast cancer treatment guidelines [4, 5]. In add- ition, these information management challenges in cancer care are occurring in a practice environment where there is little time available for tracking and accessing relevant information at the point of care [6]. For example, a study that surveyed 1117 oncolo- gists reported that on average 4.6 h per week were spent keeping VC The Author(s) 2018. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com. Annals of Oncology 29: 418–423, 2018 doi:10.1093/annonc/mdx781 Published online 9 January 2018 Downloaded from https://academic.oup.com/annonc/article-abstract/29/2/418/4781689 by guest
- 73. 잠정적 결론 •왓슨 포온콜로지와 의사의 일치율: •암종별로 다르다. •같은 암종에서도 병기별로 다르다. •같은 암종에 대해서도 병원별/국가별로 다르다. •시간이 흐름에 따라 달라질 가능성이 있다.
- 74. 원칙이 필요하다 •어떤 환자의경우, 왓슨에게 의견을 물을 것인가? •왓슨을 (암종별로) 얼마나 신뢰할 것인가? •왓슨의 의견을 환자에게 공개할 것인가? •왓슨과 의료진의 판단이 다른 경우 어떻게 할 것인가? •왓슨에게 보험 급여를 매길 수 있는가? 이러한 기준에 따라 의료의 질/치료효과가 달라질 수 있으나, 현재 개별 병원이 개별적인 기준으로 활용하게 됨
- 75.
- 76. Bone Age Assessment •M: 28 Classes • F: 20 Classes • Method: G.P. • Top3-95.28% (F) • Top3-81.55% (M)
- 77. 40 50 60 70 80 인공지능 의사 A의사 B 69.5% 63% 49.5% 정확도(%) 영상의학과 펠로우 (소아영상 세부전공) 영상의학과 2년차 전공의 인공지능 vs 의사 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380. • 총 환자의 수: 200명 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 인공지능: VUNO의 골연령 판독 딥러닝 골연령 판독에 인간 의사와 인공지능의 시너지 효과 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 78. 40 50 60 70 80 인공지능 의사 A의사 B 40 50 60 70 80 의사 A + 인공지능 의사 B + 인공지능 69.5% 63% 49.5% 72.5% 57.5% 정확도(%) 영상의학과 펠로우 (소아영상 세부전공) 영상의학과 2년차 전공의 인공지능 vs 의사 인공지능 + 의사 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380. • 총 환자의 수: 200명 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 인공지능: VUNO의 골연령 판독 딥러닝 골연령 판독에 인간 의사와 인공지능의 시너지 효과 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 79. 총 판독 시간(m) 0 50 100 150 200 w/o AI w/ AI 0 50 100 150 200 w/o AI w/ AI 188m 154m 180m 108m saving 40% of time saving 18% of time 의사 A 의사 B 골연령 판독에서 인공지능을 활용하면 판독 시간의 절감도 가능 • 총 환자의 수: 200명 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 인공지능: VUNO의 골연령 판독 딥러닝 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380. Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 80. Detection of DiabeticRetinopathy
- 81. Copyright 2016 AmericanMedical Association. All rights reserved. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD; Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB; Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD IMPORTANCE Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. OBJECTIVE To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. DESIGN AND SETTING A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. EXPOSURE Deep learning–trained algorithm. MAIN OUTCOMES AND MEASURES The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. RESULTS TheEyePACS-1datasetconsistedof9963imagesfrom4997patients(meanage,54.4 years;62.2%women;prevalenceofRDR,683/8878fullygradableimages[7.8%]);the Messidor-2datasethad1748imagesfrom874patients(meanage,57.6years;42.6%women; prevalenceofRDR,254/1745fullygradableimages[14.6%]).FordetectingRDR,thealgorithm hadanareaunderthereceiveroperatingcurveof0.991(95%CI,0.988-0.993)forEyePACS-1and 0.990(95%CI,0.986-0.995)forMessidor-2.Usingthefirstoperatingcutpointwithhigh specificity,forEyePACS-1,thesensitivitywas90.3%(95%CI,87.5%-92.7%)andthespecificity was98.1%(95%CI,97.8%-98.5%).ForMessidor-2,thesensitivitywas87.0%(95%CI,81.1%- 91.0%)andthespecificitywas98.5%(95%CI,97.7%-99.1%).Usingasecondoperatingpoint withhighsensitivityinthedevelopmentset,forEyePACS-1thesensitivitywas97.5%and specificitywas93.4%andforMessidor-2thesensitivitywas96.1%andspecificitywas93.9%. CONCLUSIONS AND RELEVANCE In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment. JAMA. doi:10.1001/jama.2016.17216 Published online November 29, 2016. Editorial Supplemental content Author Affiliations: Google Inc, Mountain View, California (Gulshan, Peng, Coram, Stumpe, Wu, Narayanaswamy, Venugopalan, Widner, Madams, Nelson, Webster); Department of Computer Science, University of Texas, Austin (Venugopalan); EyePACS LLC, San Jose, California (Cuadros); School of Optometry, Vision Science Graduate Group, University of California, Berkeley (Cuadros); Aravind Medical Research Foundation, Aravind Eye Care System, Madurai, India (Kim); Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India (Raman); Verily Life Sciences, Mountain View, California (Mega); Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts (Mega). Corresponding Author: Lily Peng, MD, PhD, Google Research, 1600 Amphitheatre Way, Mountain View, CA 94043 (lhpeng@google.com). Research JAMA | Original Investigation | INNOVATIONS IN HEALTH CARE DELIVERY (Reprinted) E1 Copyright 2016 American Medical Association. All rights reserved.
- 82. Training Set /Test Set • CNN으로 후향적으로 128,175개의 안저 이미지 학습 • 미국의 안과전문의 54명이 3-7회 판독한 데이터 • 우수한 안과전문의들 7-8명의 판독 결과와 인공지능의 판독 결과 비교 • EyePACS-1 (9,963 개), Messidor-2 (1,748 개)a) Fullscreen mode b) Hit reset to reload this image. This will reset all of the grading. c) Comment box for other pathologies you see eFigure 2. Screenshot of the Second Screen of the Grading Tool, Which Asks Graders to Assess the Image for DR, DME and Other Notable Conditions or Findings
- 83. • EyePACS-1 과 Messidor-2의 AUC = 0.991, 0.990 • 7-8명의 안과 전문의와 sensitivity, specificity 가 동일한 수준 • F-score: 0.95 (vs. 인간 의사는 0.91) Additional sensitivity analyses were conducted for sev- eralsubcategories:(1)detectingmoderateorworsediabeticreti- effects of data set size on algorithm performance were exam- ined and shown to plateau at around 60 000 images (or ap- Figure 2. Validation Set Performance for Referable Diabetic Retinopathy 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 EyePACS-1: AUC, 99.1%; 95% CI, 98.8%-99.3%A 100 High-sensitivity operating point High-specificity operating point 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 Messidor-2: AUC, 99.0%; 95% CI, 98.6%-99.5%B 100 High-specificity operating point High-sensitivity operating point Performance of the algorithm (black curve) and ophthalmologists (colored circles) for the presence of referable diabetic retinopathy (moderate or worse diabetic retinopathy or referable diabetic macular edema) on A, EyePACS-1 (8788 fully gradable images) and B, Messidor-2 (1745 fully gradable images). The black diamonds on the graph correspond to the sensitivity and specificity of the algorithm at the high-sensitivity and high-specificity operating points. In A, for the high-sensitivity operating point, specificity was 93.4% (95% CI, 92.8%-94.0%) and sensitivity was 97.5% (95% CI, 95.8%-98.7%); for the high-specificity operating point, specificity was 98.1% (95% CI, 97.8%-98.5%) and sensitivity was 90.3% (95% CI, 87.5%-92.7%). In B, for the high-sensitivity operating point, specificity was 93.9% (95% CI, 92.4%-95.3%) and sensitivity was 96.1% (95% CI, 92.4%-98.3%); for the high-specificity operating point, specificity was 98.5% (95% CI, 97.7%-99.1%) and sensitivity was 87.0% (95% CI, 81.1%-91.0%). There were 8 ophthalmologists who graded EyePACS-1 and 7 ophthalmologists who graded Messidor-2. AUC indicates area under the receiver operating characteristic curve. Research Original Investigation Accuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy Results
- 84. •2018년 4월 FDA는안저사진을 판독하여 당뇨성 망막병증(DR)을 진단하는 인공지능 시판 허가 •IDx-DR: 클라우드 기반의 소프트웨어로, Topcon NW400 로 찍은 사진을 판독 •의사의 개입 없이 안저 사진을 판독하여 DR 여부를 진단 •두 가지 답 중에 하나를 준다 •1) mild DR 이상이 detection 되었으니, 의사에게 가봐라 •2) mild DR 이상은 없는 것 같으니, 12개월 이후에 다시 검사 받아봐라 •임상시험 및 성능 •10개의 병원에서 멀티센터로 900명 환자의 데이터를 분석 •민감도와 특이도가 각각 87.4%, 89.5% (JAMA 논문의 구글 인공지능 보다 낮음) •FDA가 de novo premarket review pathway로 진행
- 85.
- 86. 0 0 MO N T H 2 0 1 7 | V O L 0 0 0 | N A T U R E | 1 LETTER doi:10.1038/nature21056 Dermatologist-level classification of skin cancer with deep neural networks Andre Esteva1 *, Brett Kuprel1 *, Roberto A. Novoa2,3 , Justin Ko2 , Susan M. Swetter2,4 , Helen M. Blau5 & Sebastian Thrun6 Skin cancer, the most common human malignancy1–3 , is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs)4,5 show potential for general and highly variable tasks across many fine-grained object categories6–11 . Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets12 —consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care. There are 5.4 million new cases of skin cancer in the United States2 every year. One in five Americans will be diagnosed with a cutaneous malignancy in their lifetime. Although melanomas represent fewer than 5% of all skin cancers in the United States, they account for approxi- mately 75% of all skin-cancer-related deaths, and are responsible for over 10,000 deaths annually in the United States alone. Early detection is critical, as the estimated 5-year survival rate for melanoma drops from over 99% if detected in its earliest stages to about 14% if detected in its latest stages. We developed a computational method which may allow medical practitioners and patients to proactively track skin lesions and detect cancer earlier. By creating a novel disease taxonomy, and a disease-partitioning algorithm that maps individual diseases into training classes, we are able to build a deep learning system for auto- mated dermatology. Previous work in dermatological computer-aided classification12,14,15 has lacked the generalization capability of medical practitioners owing to insufficient data and a focus on standardized tasks such as dermoscopy16–18 and histological image classification19–22 . Dermoscopy images are acquired via a specialized instrument and histological images are acquired via invasive biopsy and microscopy; whereby both modalities yield highly standardized images. Photographic images (for example, smartphone images) exhibit variability in factors such as zoom, angle and lighting, making classification substantially more challenging23,24 . We overcome this challenge by using a data- driven approach—1.41 million pre-training and training images make classification robust to photographic variability. Many previous techniques require extensive preprocessing, lesion segmentation and extraction of domain-specific visual features before classification. By contrast, our system requires no hand-crafted features; it is trained end-to-end directly from image labels and raw pixels, with a single network for both photographic and dermoscopic images. The existing body of work uses small datasets of typically less than a thousand images of skin lesions16,18,19 , which, as a result, do not generalize well to new images. We demonstrate generalizable classification with a new dermatologist-labelled dataset of 129,450 clinical images, including 3,374 dermoscopy images. Deep learning algorithms, powered by advances in computation and very large datasets25 , have recently been shown to exceed human performance in visual tasks such as playing Atari games26 , strategic board games like Go27 and object recognition6 . In this paper we outline the development of a CNN that matches the performance of dermatologists at three key diagnostic tasks: melanoma classification, melanoma classification using dermoscopy and carcinoma classification. We restrict the comparisons to image-based classification. We utilize a GoogleNet Inception v3 CNN architecture9 that was pre- trained on approximately 1.28 million images (1,000 object categories) from the 2014 ImageNet Large Scale Visual Recognition Challenge6 , and train it on our dataset using transfer learning28 . Figure 1 shows the working system. The CNN is trained using 757 disease classes. Our dataset is composed of dermatologist-labelled images organized in a tree-structured taxonomy of 2,032 diseases, in which the individual diseases form the leaf nodes. The images come from 18 different clinician-curated, open-access online repositories, as well as from clinical data from Stanford University Medical Center. Figure 2a shows a subset of the full taxonomy, which has been organized clinically and visually by medical experts. We split our dataset into 127,463 training and validation images and 1,942 biopsy-labelled test images. To take advantage of fine-grained information contained within the taxonomy structure, we develop an algorithm (Extended Data Table 1) to partition diseases into fine-grained training classes (for example, amelanotic melanoma and acrolentiginous melanoma). During inference, the CNN outputs a probability distribution over these fine classes. To recover the probabilities for coarser-level classes of interest (for example, melanoma) we sum the probabilities of their descendants (see Methods and Extended Data Fig. 1 for more details). We validate the effectiveness of the algorithm in two ways, using nine-fold cross-validation. First, we validate the algorithm using a three-class disease partition—the first-level nodes of the taxonomy, which represent benign lesions, malignant lesions and non-neoplastic 1 Department of Electrical Engineering, Stanford University, Stanford, California, USA. 2 Department of Dermatology, Stanford University, Stanford, California, USA. 3 Department of Pathology, Stanford University, Stanford, California, USA. 4 Dermatology Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA. 5 Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. 6 Department of Computer Science, Stanford University, Stanford, California, USA. *These authors contributed equally to this work. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
- 87. LETTERH his task, theCNN achieves 72.1±0.9% (mean±s.d.) overall he average of individual inference class accuracies) and two gists attain 65.56% and 66.0% accuracy on a subset of the set. Second, we validate the algorithm using a nine-class rtition—the second-level nodes—so that the diseases of have similar medical treatment plans. The CNN achieves two trials, one using standard images and the other using images, which reflect the two steps that a dermatologist m to obtain a clinical impression. The same CNN is used for a Figure 2b shows a few example images, demonstrating th distinguishing between malignant and benign lesions, whic visual features. Our comparison metrics are sensitivity an Acral-lentiginous melanoma Amelanotic melanoma Lentigo melanoma … Blue nevus Halo nevus Mongolian spot … Training classes (757)Deep convolutional neural network (Inception v3) Inference classes (varies by task) 92% malignant melanocytic lesion 8% benign melanocytic lesion Skin lesion image Convolution AvgPool MaxPool Concat Dropout Fully connected Softmax Deep CNN layout. Our classification technique is a Data flow is from left to right: an image of a skin lesion e, melanoma) is sequentially warped into a probability over clinical classes of skin disease using Google Inception hitecture pretrained on the ImageNet dataset (1.28 million 1,000 generic object classes) and fine-tuned on our own 29,450 skin lesions comprising 2,032 different diseases. ning classes are defined using a novel taxonomy of skin disease oning algorithm that maps diseases into training classes (for example, acrolentiginous melanoma, amelanotic melano melanoma). Inference classes are more general and are comp or more training classes (for example, malignant melanocytic class of melanomas). The probability of an inference class is c summing the probabilities of the training classes according to structure (see Methods). Inception v3 CNN architecture repr from https://research.googleblog.com/2016/03/train-your-ow classifier-with.html GoogleNet Inception v3 • 129,450개의 피부과 병변 이미지 데이터를 자체 제작 • 미국의 피부과 전문의 18명이 데이터 curation • CNN (Inception v3)으로 이미지를 학습 • 피부과 전문의들 21명과 인공지능의 판독 결과 비교 • 표피세포 암 (keratinocyte carcinoma)과 지루각화증(benign seborrheic keratosis)의 구분 • 악성 흑색종과 양성 병변 구분 (표준 이미지 데이터 기반) • 악성 흑색종과 양성 병변 구분 (더마토스코프로 찍은 이미지 기반)
- 88. Skin cancer classificationperformance of the CNN and dermatologists. LETT a b 0 1 Sensitivity 0 1 Specificity Melanoma: 130 images 0 1 Sensitivity 0 1 Specificity Melanoma: 225 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 111 dermoscopy images 0 1 Sensitivity 0 1 Specificity Carcinoma: 707 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 1,010 dermoscopy images Algorithm: AUC = 0.94 0 1 Sensitivity 0 1 Specificity Carcinoma: 135 images Algorithm: AUC = 0.96 Dermatologists (25) Average dermatologist Algorithm: AUC = 0.94 Dermatologists (22) Average dermatologist Algorithm: AUC = 0.91 Dermatologists (21) Average dermatologist cancer classification performance of the CNN and 21명 중에 인공지능보다 정확성이 떨어지는 피부과 전문의들이 상당수 있었음 피부과 전문의들의 평균 성적도 인공지능보다 좋지 않았음
- 89.
- 90. Assisting Pathologists inDetecting Cancer with Deep Learning • The localization score(FROC) for the algorithm reached 89%, which significantly exceeded the score of 73% for a pathologist with no time constraint. https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
- 91. Assisting Pathologists inDetecting Cancer with Deep Learning • Algorithms need to be incorporated in a way that complements the pathologist’s workflow. • Algorithms could improve the efficiency and consistency of pathologists. • For example, pathologists could reduce their false negative rates (percentage of undetected tumors) by reviewing the top ranked predicted tumor regions including up to 8 false positive regions per slide. https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
- 92. Assisting Pathologists inDetecting Cancer with Deep Learning 6 Input & Validation Test model size FROC @8FP AUC FROC @8FP AUC 40X 98.1 100 99.0 87.3 (83.2, 91.1) 91.1 (87.2, 94.5) 96.7 (92.6, 99.6) 40X-pretrained 99.3 100 100 85.5 (81.0, 89.5) 91.1 (86.8, 94.6) 97.5 (93.8, 99.8) 40X-small 99.3 100 100 86.4 (82.2, 90.4) 92.4 (88.8, 95.7) 97.1 (93.2, 99.8) ensemble-of-3 - - - 88.5 (84.3, 92.2) 92.4 (88.7, 95.6) 97.7 (93.0, 100) 20X-small 94.7 100 99.6 85.5 (81.0, 89.7) 91.1 (86.9, 94.8) 98.6 (96.7, 100) 10X-small 88.7 97.2 97.7 79.3 (74.2, 84.1) 84.9 (80.0, 89.4) 96.5 (91.9, 99.7) 40X+20X-small 94.9 98.6 99.0 85.9 (81.6, 89.9) 92.9 (89.3, 96.1) 97.0 (93.1, 99.9) 40X+10X-small 93.8 98.6 100 82.2 (77.0, 86.7) 87.6 (83.2, 91.7) 98.6 (96.2, 99.9) Pathologist [1] - - - 73.3* 73.3* 96.6 Camelyon16 winner [1, 23] - - - 80.7 82.7 99.4 Table 1. Results on Camelyon16 dataset (95% confidence intervals, CI). Bold indicates results within the CI of the best model. “Small” models contain 300K parameters per Inception tower instead of 20M. -: not reported. *A pathologist achieved this sensitivity (with no FP) using 30 hours. to 10 20% variance), and can confound evaluation of model improvements by grouping multiple nearby tumors as one. By contrast, our non-maxima sup- pression approach is relatively insensitive to r between 4 and 6, although less accurate models benefited from tuning r using the validation set (e.g., 8). Fi- The FROC evaluates tumor detection and localization The FROC is defined as the sensitivity at 0.25,0.5,1,2,4,8 average FPs per tumor-negative slide. Yun Liu et al. Detecting Cancer Metastases on Gigapixel Pathology Images (2017) Sensitivity at 8 false positives per image
- 93. Assisting Pathologists inDetecting Cancer with Deep Learning Yun Liu et al. Detecting Cancer Metastases on Gigapixel Pathology Images (2017) • 구글의 인공지능은 @8FP 및 FROC에서 큰 개선 (92.9%, 88.5%) •@8FP: FP를 8개까지 봐주면서, 달성할 수 있는 sensitivity •FROC: FP를 슬라이드당 1/4, 1/2, 1, 2, 4, 8개를 허용한 민감도의 평균 •즉, FP를 조금 봐준다면, 인공지능은 매우 높은 민감도를 달성 가능 • 인간 병리학자는 민감도 73%에 반해 특이도는 거의 100% 달성 •인간 병리학자와 인공지능 병리학자는 서로 잘하는 것이 다름 •양쪽이 협력하면 판독 효율성, 일관성, 민감도 등에서 개선 기대 가능
- 94.
- 95.
- 96. AACR 2018인공지능을 이용하면총 판독 시간을 줄일 수 있다
- 97. AACR 2018인공지능을 이용하면판독 정확도를 (micro에서 특히) 높일 수 수 있다
- 98.
- 99. BeyondVerbal: Reading emotionsfrom voices
- 100.
- 102. BeyondVerbal • 기계가 사람의감정을 이해한다면? • 헬스케어 분야에서도 응용도 높음: 슬픔/우울함/피로 등의 감정 파악 • 보험 회사에서는 가입자의 우울증 여부 파악을 위해 이미 사용 중 • Aetna 는 2012년 부터 고객의 우울증 여부를 전화 목소리 분석으로 파악 • 기존의 방식에 비해 우울증 환자 6배 파악 • 사생활 침해 여부 존재
- 103. • linguistic • identificationand extraction of word instances (unigrams) and word-pair instances (bi-grams) from the transcriptions • acoustic • vocal dynamics • voice quality • vocal tract resonance frequencies • pause lengths A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects:A Prospective Multicenter Trial • “Do you have hope?” • “Do you have any fear?” • “Do you have any secrets?” • “Are you angry?” • “Does it hurt emotionally?” Pestian, Suicide and Life-Threatening Behavior, 2016
- 104. A Machine LearningApproach to Identifying the Thought Markers of Suicidal Subjects:A Prospective Multicenter Trial SensitivitySensitivity 1.00.00.20.40.60.81.00.00.20.40.60.81.0 1.0 0.8 0.6 0.4 0.2 0.0 Specificity 1.0 0.8 0.6 0.4 0.2 0.0 Specificity SUICIDE THOUGHT MARKERS SensitivitySensitivitySensitivity 0.00.20.40.60.81.00.00.20.40.60.81.00.00.20.40.60.81.0 1.0 0.8 0.6 0.4 0.2 0.0 Specificity 1.0 0.8 0.6 0.4 0.2 0.0 Specificity 1.0 0.8 0.6 0.4 0.2 0.0 Specificity Figure 1. Receiver operator curve (ROC): suicide versus control (upper), suicide versus mentally ill (middle), and SensitivitySensitivity 0.00.20.40.60.81.00.00.20.40.60.81.0 1.0 0.8 0.6 0.4 0.2 0.0 Specificity 1.0 0.8 0.6 0.4 0.2 0.0 Specificity Figure 1. Receiver operator curve (ROC): suicide versus control (upper), suicide versus mentally suicide versus mentally ill with control. The ROC curves for adolescents (blue), adults (red), and a generated where the nonsuicidal population is controls (top), mentally ill (middle), and mentally using linguistic and acoustic features. The gray line is the AROC curve for a baseline (random) cla TABLE 2 The AROC for the Machine Learning Algorithm. The Nonsuicidal Group Comprises of Either Mentally Ill and Control Subjects. Classification Performances are Shown for Adolescents, Adults, and the Combined Adolescent and Adult Cohorts Suicidal versus Controls Suicidal versus Mentally Ill Suicidal versus Mentally Ill and Controls Adolescents ROC (SD) Adults ROC (SD) Adolescents + Adults ROC (SD) Adolescents ROC (SD) Adults ROC (SD) Adolescents + Adults ROC (SD) Adolescents ROC (SD) Adults ROC (SD) Adolescents + Adults ROC (SD) Linguistics 0.87 (0.04) 0.91 (0.02) 0.93 (0.02) 0.82 (0.05) 0.77 (0.04) 0.79 (0.03) 0.82 (0.04) 0.84 (0.03) 0.87 (0.02) Acoustics 0.74 (0.05) 0.82 (0.03) 0.79 (0.03) 0.69 (0.06) 0.74 (0.04) 0.76 (0.03) 0.74 (0.05) 0.80 (0.03) 0.76 (0.03) Linguistics + Acoustics 0.83 (0.05) 0.93 (0.02) 0.92 (0.02) 0.80 (0.05) 0.77 (0.04) 0.82 (0.03) 0.81 (0.04) 0.84 (0.03) 0.87 (0.02) PESTIANETAL. Suicidal vs. Control Suicidal vs. Mentally Ill Suicidal vs. Mentally Ill and Controls adolescents adults Pestian, Suicide and Life-Threatening Behavior, 2016
- 105. “A lot ofSyrian refugees have trauma and maybe this can help them overcome that.” However, he points out that there is a stigma around psychotherapy, saying people feel shame about seeking out psychologists. As a result he thinks people might feel more comfortable knowing they are talking to a “robot” than to a human.
- 106. http://www.newyorker.com/tech/elements/the-chatbot-will-see-you-now •시리아 난민의 3/4정도가 불안감, 고립감, 불면증 등의 증세 •아랍어를 하는 수천명의 상담사를 구하기는 불가능 •실리콘밸리 스타트업 X2AI 의 챗봇, Karim •emotion-recognition algorithm •표현, 용어 사용, 타이핑 속도, 문장의 길이, 문법 (수동태, 능동태) 등의 파라미터를 분석 •분노, 슬픔을 표현하는 것을 금기시하는 시리아에서, 오히려 환자들이 챗봇과 상담하는 것을 편하게 느낌 •스탠퍼드 심리학과 David Spiegel 교수 •진단에 관한 역량은 유망할 것; 모든 상담 내용 기억 •케어받는 느낌을 주거나, 전이감정 다루기는 어려울 것
- 107. • Woebot, 정신상담 챗봇 스타트업 • 스탠퍼드의 mental health 전문가들이 시작한 우울증 치료 (인지행동치료) 목적의 챗봇 • Andrew Ng 교수는 이사회장으로 참여
- 108. • Woebot, 정신상담 챗봇 • 실제 상담사들이 하듯이, 대화형으로 설명하고 사용자의 정신 건강 상태를 체크 • 대부분 설문과 다를 것이 없지만 (정해진 답 중에 하나 선택), UI 상의 혁신이라고 볼 수 있음 • 아직까지는 아주 정교한 NLP를 사용하고 있지는 않음 (세션 당 한 번 정도)
- 109. • Woebot, 정신상담 챗봇 • 실제 상담사들이 하듯이, 대화형으로 설명하고 사용자의 정신 건강 상태를 체크 • 대부분 설문과 다를 것이 없지만 (정해진 답 중에 하나 선택), UI 상의 혁신이라고 볼 수 있음 • 아직까지는 아주 정교한 NLP를 사용하고 있지는 않음 (세션 당 한 번 정도)
- 110. • Woebot, 정신상담 챗봇 • 실제 상담사들이 하듯이, 대화형으로 설명하고 사용자의 정신 건강 상태를 체크 • 대부분 설문과 다를 것이 없지만 (정해진 답 중에 하나 선택), UI 상의 혁신이라고 볼 수 있음 • 아직까지는 아주 정교한 NLP를 사용하고 있지는 않음 (세션 당 한 번 정도)
- 111. Original Paper Delivering CognitiveBehavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial Kathleen Kara Fitzpatrick1* , PhD; Alison Darcy2* , PhD; Molly Vierhile1 , BA 1 Stanford School of Medicine, Department of Psychiatry and Behavioral Sciences, Stanford, CA, United States 2 Woebot Labs Inc., San Francisco, CA, United States * these authors contributed equally Corresponding Author: Alison Darcy, PhD Woebot Labs Inc. 55 Fair Avenue San Francisco, CA, 94110 United States Email: alison@woebot.io Abstract Background: Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time. Objective: The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression. Methods: In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,” as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Results: Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants’ comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy. Conclusions: Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT. (JMIR Ment Health 2017;4(2):e19) doi:10.2196/mental.7785 KEYWORDS conversational agents; mobile mental health; mental health; chatbots; depression; anxiety; college students; digital health Introduction Up to 74% of mental health diagnoses have their first onset particularly common among college students, with more than half reporting symptoms of anxiety and depression in the previous year that were so severe they had difficulty functioning Fitzpatrick et alJMIR MENTAL HEALTH
- 112. depression at baselineas measured by the PHQ-9, while three-quarters (74%, 52/70) were in the severe range for anxiety as measured by the GAD-7. Figure 1. Participant recruitment flow. Table 1. Demographic and clinical variables of participants at baseline. WoebotInformation control Scale, mean (SD) 14.30 (6.65)13.25 (5.17)Depression (PHQ-9) 18.05 (5.89)19.02 (4.27)Anxiety (GAD-7) 25.54 (9.58)26.19 (8.37)Positive affect 24.87 (8.13)28.74 (8.92)Negative affect 22.58 (2.38)21.83 (2.24)Age, mean (SD) Gender, n (%) 7 (21)4 (7)Male 27 (79)20 (55)Female Ethnicity, n (%) 2 (6)2 (8)Latino/Hispanic 32 (94)22 (92)Non-Latino/Hispanic 28 (82)18 (75)Caucasian Fitzpatrick et alJMIR MENTAL HEALTH Delivering Cognitive Behavior Therapy toYoung Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot):A Randomized Controlled Trial •분노장애와 우울증이 있다고 스스로 생각하는 대학생들이 사용하는 self-help 챗봇 •목적: 챗봇의 feasibility, acceptability, preliminary efficacy 를 보기 위함 •대학생 총 70명을 대상으로 2주 동안 진행 •실험군 (Woebot): 34명 •대조군 (information-only): 31명 •Oucome: PHQ-9, GAD-7
- 113. d cPFWoebotInformation-only control 95%CIb T2a 95% CIb T2a 0.44.0176.039.74-12.3211.14 (0.71)12.07-15.2713.67 (.81)PHQ-9 0.14.5810.3816.16-18.1317.35 (0.60)15.52-18.5616.84 (.67)GAD-7 0.02.7070.1724.35-29.4126.88 (1.29)23.17-28.8626.02 (1.45)PANAS positive affect 0.344.9120.9123.54-28.4225.98 (1.24)24.73-30.3227.53 (1.42)PANAS nega- tive affect a Baseline=pooled mean (standard error) b 95% confidence interval. c Cohen d shown for between-subjects effects using means and standard errors at Time 2. Figure 2. Change in mean depression (PHQ-9) score by group over the study period. Error bars represent standard error. Preliminary Efficacy Table 2 shows the results of the primary ITT analyses conducted on the entire sample. Univariate ANCOVA revealed a significant treatment effect on depression revealing that those in the Woebot group significantly reduced PHQ-9 score while those in the information control group did not (F1,48=6.03; P=.017) (see Figure 2). This represented a moderate between-groups effect size (d=0.44). This effect is robust after Bonferroni correction for multiple comparisons (P=.04). No other significant between-group differences were observed on anxiety or affect. Completer Analysis As a secondary analysis, to explore whether any main effects existed, 2x2 repeated measures ANOVAs were conducted on the primary outcome variables (with the exception of PHQ-9) among completers only. A significant main effect was observed on GAD-7 (F1,54=9.24; P=.004) suggesting that completers experienced a significant reduction in symptoms of anxiety between baseline and T2, regardless of the group to which they were assigned with a within-subjects effect size of d=0.37. No main effects were observed for positive (F1,50=.001; P=.951; d=0.21) or negative affect (F1,50=.06; P=.80; d=0.003) as measured by the PANAS. To further elucidate the source and magnitude of change in depression, repeated measures dependent t tests were conducted and Cohen d effect sizes were calculated on individual items of the PHQ-9 among those in the Woebot condition. The analysis revealed that baseline-T2 changes were observed on the following items in order of decreasing magnitude: motoric symptoms (d=2.09), appetite (d=0.65), little interest or pleasure in things (d=0.44), feeling bad about self (d=0.40), and concentration (d=0.39), and suicidal thoughts (d=0.30), feeling down (d=0.14), sleep (d=0.12), and energy (d=0.06). JMIR Ment Health 2017 | vol. 4 | iss. 2 | e19 | p.6http://mental.jmir.org/2017/2/e19/ (page number not for citation purposes) XSL•FO RenderX Change in mean depression (PHQ-9) score by group over the study period •결과 •챗봇을 2주 동안 평균 12.14번 사용함 •우울증에 대해서는 significant group difference •Woebot 그룹에서는 우울증(PHQ-9)의 유의미한 감소가 있었음 •대조군에서는 유의미한 감소 없음 •분노 장애에 대해서는 두 그룹 모두 유의미한 감소가 있었음 (GAD-7 기준)
- 114. •Woebot은 2017년 많은데이터를 수집하면서 성장 •페북 메신져로만 서비스 하였음에도 불구하고, 매달 50% 이상 성장, •주당 2m 개 이상의 메시지 축적, 130개국 이상의 사용자 확보 •2018년 초 iOS 앱 및 안드로이드 앱 출시 •2018년 3월 Woebot 이 $8m의 시리즈A 투자에 성공 •New Enterprise Associates (NEA)가 리드하고 Andrew Ng 교수의 AI Fund 도 참여 •AI Fund: Andrew Ng 교수가 올해 1월 결성한 AI 스타트업에 투자하는 $175M 규모 펀드
- 115. G.M. Lucas etal. / Computers in Human Behavior 37 (2014) 94–100 인간 의사와 인공지능 의사 중 누가 라뽀 형성을 더 잘 할까?
- 116.
- 117.
- 118. It’s only acomputer: Virtual humans increase willingness to disclose G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100 인공지능이 상담한다고 믿음 (computer frame) 사람이 원격으로 상담한다고 믿음 (human frame) 실제로 인공지능이 상담 (AI) 실제로 사람이 상담 (Tele-operated) Method Frame
- 119. It’s only acomputer: Virtual humans increase willingness to disclose G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100 ‘‘How close are you to your family?’’ ‘‘Tell me about a situation that you wish you had handled differently.’’ ‘‘Tell me about an event, or something that you wish you could erase from your memory.’’ ‘‘Tell me about the hardest decision you’ve ever had to make.’’ ‘‘Tell me about the last time you felt really happy.’’ ‘‘What are you most proud of in your life?’’ ‘‘What’s something you feel guilty about?’’ ‘‘When was the last time you argued with someone and what was it about?’’
- 120. It’s only acomputer: Virtual humans increase willingness to disclose G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100 0 5 10 15 20 Computer frame Human frame 0 15.25 30.5 45.75 61 Computer frame Human frame 0 0.033 0.065 0.098 0.13 Computer frame Human frame 0 0.3 0.6 0.9 1.2 Computer frame Human frame Fear of Self-disclosure Impression Management Sadness Displays Willingness to Disclosure
- 121. It’s only acomputer: Virtual humans increase willingness to disclose G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100 ‘‘This is way better than talking to a person. I don’t really feel comfortable talking about personal stuff to other people.’’ ‘‘A human being would be judgmental. I shared a lot of personal things, and it was because of that.’’
- 122.
- 123.
- 124. •Digiceutical = digital+ pharmaceutical •"chemical 과 protein에 이어서 digital drug 이 세번째 종류의 신약이 될 것이다” •digital drug 은 크게 두 가지 종류 •기존의 약을 아예 대체 •기존 약을 강화(augment)
- 125.
- 126. • reSET® wasevaluated in a clinical trial of 507 patients with SUD across 10 treatment centers nation-wide over 12 weeks.* • In patients who were dependent on stimulants, marijuana, cocaine, or alcohol (n=395), 58.1% of patients receiving reSET®* were abstinent in study weeks 9-12, while 29.8% of patients receiving face-to-face therapy alone were abstinent during the same time frame (p<0.01). • Participants who tested positive for drug use at the start of the study (n=191), 26.7% of patients receiving reSET®* were abstinent in study weeks 9-12, while 3.2% of patients receiving traditional face-to-face therapy were abstinent during the same time frame (p<0.01). Pear Therapeutics Campbell et al. Am J Psychiatry. 2014.
- 127. Campbell et al.Am J Psychiatry. 2014. Pear Therapeutics • Patients receiving reSET® showed statistically significant improvement in retention compared to face-to-face therapy alone (p=0.0316).At the end of 12 weeks of treatment 59% of patients receiving face-to-face therapy were retained in the study compared to 67% of patients receiving reSET®.
- 128. Pear Therapeutics •최초로 스마트폰앱이 digital therapeutics 로 질병 치료 목적으로 FDA de novo clearance (기기 없이 '앱'만으로 구성된 시스템이 '질병 치료' 목적으로 허가 받은 것은 최초) •Pear Therapeutics의 reSET 이라는 시스템으로 각종 중독을 치료하는 목적의 앱 •12주에 걸쳐서 대마, 코카인, 알콜 중독에 대한 중독과 의존성을 치료
- 131. 14© 2017 byHURAYPOSITIVE INC., a Digital Healthcare Service Provider. This information is strictly privileged and confidential. All rights reserved. 제2형 당뇨병 환자 95% 임신성 당뇨병 환자 2% 기타 1% 정상인 당뇨병 전단계 환자 당뇨병 환자 경증합병증 동반 당뇨병 환자 중증합병증 동반 당뇨병 환자 제1형 당뇨병 환자 2% 보건복지부/건강보험공단 (국민건강증진 및 관리) 병원/제약사/보험사 (비용절감 및 고객만족) 차기 위험단계로의 적극적인 진입 억제를 위한 헬스케어 솔루션 휴레이포지티브 헬스케어 솔루션 $ key facts Products & Services 서비스 대상 & 역할
- 132. 16© 2017 byHURAYPOSITIVE INC., a Digital Healthcare Service Provider. This information is strictly privileged and confidential. All rights reserved. 7 7.2 7.4 7.6 7.8 8 8.2 3M 6M 9M 12M0M ▼0.63%p. ▼0.64%p. 당화혈색소(HbA1c,%) & Products & Services 의학적 유효성(Health Switch를 활용한 임상실험) 기간 • 1차 실험(0M-6M) 실험군: 중재 O ( ) 대조군: 중재 X ( ) • 2차 실험: 실험군과 대조군 교차(6M-12M) 대조군: 중재 X ( ) 실험군: 중재 O ( ) 당화혈색소 0.63%p. 감소 무의미한 변화 당화혈색소 수준 유지 당화혈색소 0.64%p. 감소 ▼0.04%p. • N = 148명 • 평균 연령: 52.2세 결과 임상 대상자 1 모바일 중재 서비스의 의미 있는 혈당 감소 효과 2 약 6개월의 서비스 후 생활습관 유지 가능성 3 고령 환자들도 사용할 수 있는 간편한 서비스 임상실험을 통해 검증된 Health Switch의 효과 key facts • 특징: 제2형 당뇨병 유병자 • 기간: 2014.10 ~ 2015.12
- 133. 1SCIENTIFIC REPORTS |(2018) 8:3642 | DOI:10.1038/s41598-018-22034-0 www.nature.com/scientificreports The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders:A randomized, controlled, open-label study DaYoung Lee1,2 , Jeongwoon Park3 , DooahChoi3 , Hong-YupAhn4 , Sung-Woo Park1 & Cheol-Young Park 1 This randomized, controlled, open-label study conducted in Kangbuk Samsung Hospital evaluated the effectiveness, reproducibility, and durability of tailored mobile coaching (TMC) on diabetes management.The participants included 148 Korean adult policyholders with type 2 diabetes divided into the Intervention-Maintenance (I-M) group (n=74) andControl-Intervention (C-I) group (n=74). Intervention was the addition ofTMC to typical diabetes care. In the 6-month phase 1, the I-M group receivedTMC, and theC-I group received their usual diabetes care. During the second 6-month phase 2, theC-I group receivedTMC, and the I-M group received only regular information messages.After the 6-month phase 1, a significant decrease (0.6%) in HbA1c levels compared with baseline values was observed in only the I-M group (from 8.1±1.4% to 7.5±1.1%, P<0.001 based on a paired t-test). At the end of phase 2, HbA1c levels in theC-I group decreased by 0.6% compared with the value at 6 months (from 7.9±1.5 to 7.3±1.0, P<0.001 based on a paired t-test). In the I-M group, no changes were observed. Both groups showed significant improvements in frequency of blood-glucose testing and exercise. In conclusion, addition ofTMC to conventional treatment for diabetes improved glycemic control, and this effect was maintained without individualized message feedback. The incidence and prevalence of type 2 diabetes are increasing rapidly worldwide, and the disease is expected to affect 439 million adults by 20301 . Previous large clinical trials indicated that adequate glycemic control con- tributed to a reduction in both microvascular and macrovascular complications as well as mortality rates due to diabetes2,3 . Complications from diabetes result in greater expenditure and reduced productivity. Therefore, it is a socioeconomic concern4,5 . Adequate glycemic control is important not only as an individual health problem, but also as a challenge to healthcare systems worldwide. However, approximately 40% of subjects with diabetes in the United States do not meet the recommended target for glycemic control, low-density lipoprotein cholesterol (LDL-C) level, or blood pressure (BP)6 . In Korea, glycated hemoglobin (HbA1c) levels for nearly half of diabetic patients were above 7.0%7 . Although successful diabetes care requires therapeutic lifestyle modification in addition to proper medica- tion8–10 , only 55% of individuals with type 2 diabetes receive diabetes education from healthcare professionals11 , and 16% report adhering to recommended self-management activities9 . Multifaceted professional inter- ventions are needed to support patient efforts for behavior change including healthy lifestyle choices, disease self-management, and prevention of diabetes complications10 . 1 Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, SungkyunkwanUniversitySchool of Medicine,Seoul, Republic of Korea.2 Division of Endocrinology and Metabolism, Department of Internal Medicine, KoreaUniversityCollege of Medicine,Seoul, Republic of Korea.3 Huraypositive Inc. Sinsa-dong, Gangnam-gu, Seoul, Republic of Korea. 4 Department of Statistics, Dongguk University-Seoul, Seoul, Republic of Korea. Correspondence and requests for materials should be addressed to C.-Y.P. (email: cydoctor@ chol.com) Received: 29 November 2017 Accepted: 15 February 2018 Published: xx xx xxxx OPEN
- 134. 1SCIENTIFIC REPORTS |(2018) 8:3642 | DOI:10.1038/s41598-018-22034-0 www.nature.com/scientificreports The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders:A randomized, controlled, open-label study DaYoung Lee1,2 , Jeongwoon Park3 , DooahChoi3 , Hong-YupAhn4 , Sung-Woo Park1 & Cheol-Young Park 1 This randomized, controlled, open-label study conducted in Kangbuk Samsung Hospital evaluated the effectiveness, reproducibility, and durability of tailored mobile coaching (TMC) on diabetes management.The participants included 148 Korean adult policyholders with type 2 diabetes divided into the Intervention-Maintenance (I-M) group (n=74) andControl-Intervention (C-I) group (n=74). Intervention was the addition ofTMC to typical diabetes care. In the 6-month phase 1, the I-M group receivedTMC, and theC-I group received their usual diabetes care. During the second 6-month phase 2, theC-I group receivedTMC, and the I-M group received only regular information messages.After the 6-month phase 1, a significant decrease (0.6%) in HbA1c levels compared with baseline values was observed in only the I-M group (from 8.1±1.4% to 7.5±1.1%, P<0.001 based on a paired t-test). At the end of phase 2, HbA1c levels in theC-I group decreased by 0.6% compared with the value at 6 months (from 7.9±1.5 to 7.3±1.0, P<0.001 based on a paired t-test). In the I-M group, no changes were observed. Both groups showed significant improvements in frequency of blood-glucose testing and exercise. In conclusion, addition ofTMC to conventional treatment for diabetes improved glycemic control, and this effect was maintained without individualized message feedback. The incidence and prevalence of type 2 diabetes are increasing rapidly worldwide, and the disease is expected to affect 439 million adults by 20301 . Previous large clinical trials indicated that adequate glycemic control con- tributed to a reduction in both microvascular and macrovascular complications as well as mortality rates due to diabetes2,3 . Complications from diabetes result in greater expenditure and reduced productivity. Therefore, it is a socioeconomic concern4,5 . Adequate glycemic control is important not only as an individual health problem, but also as a challenge to healthcare systems worldwide. However, approximately 40% of subjects with diabetes in the United States do not meet the recommended target for glycemic control, low-density lipoprotein cholesterol (LDL-C) level, or blood pressure (BP)6 . In Korea, glycated hemoglobin (HbA1c) levels for nearly half of diabetic patients were above 7.0%7 . Although successful diabetes care requires therapeutic lifestyle modification in addition to proper medica- tion8–10 , only 55% of individuals with type 2 diabetes receive diabetes education from healthcare professionals11 , and 16% report adhering to recommended self-management activities9 . Multifaceted professional inter- ventions are needed to support patient efforts for behavior change including healthy lifestyle choices, disease self-management, and prevention of diabetes complications10 . 1 Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, SungkyunkwanUniversitySchool of Medicine,Seoul, Republic of Korea.2 Division of Endocrinology and Metabolism, Department of Internal Medicine, KoreaUniversityCollege of Medicine,Seoul, Republic of Korea.3 Huraypositive Inc. Sinsa-dong, Gangnam-gu, Seoul, Republic of Korea. 4 Department of Statistics, Dongguk University-Seoul, Seoul, Republic of Korea. Correspondence and requests for materials should be addressed to C.-Y.P. (email: cydoctor@ chol.com) Received: 29 November 2017 Accepted: 15 February 2018 Published: xx xx xxxx OPEN e.com/scientificreports/ Figure 3. Changes in means and standard errors of glycated hemoglobin (H study period. HbA1c levels of the C-I group who received TMC during phase 2 of the study decreased by 0.6% compared to phase 1 levels. In the I-M group, initial improvement in HbA1c levels at 3 months continued until 12 months. Consequently, HbA1c levels in both the C-I and I-M groups decreased significantly compared to baseline values over the 12-month study period.
- 135.
- 136.
- 137. Prolonged Exposure Therapy (지속 노출 치료)
- 138. 지속 노출 치료의한계 • 환자들이 트라우마를 떠올리는 것에 거부감을 느끼거나, 효과적으로 상상하지 못함 • 사실 그 자체가 PTSD 의 증상의 하나 • 환자가 트라우마에 대한 기억을 생생하게 시각화하지 못하면 치료 효과 감소 어떻게 환자에게 실감나는 상황을 시각화 해줄 것인가
- 139.
- 140. VirtualVietnam •VR은 PTSD의 치료를위해 1990년대부터 활용 •최초의 시도: 버추얼 베트남 (1997) • 정글을 헤치고 나가는 시나리오 / 군용 헬리곱터가 날아가는 시나리오 • 그래픽 수준, 구현 효과 및 시나리오 등이 제한적 • 전통적 심리 치료에 효과 없던 환자 전원이 유의미한 개선 효과 “영상 속에서 베트남 사람들과 탱크를 보았어요”
- 141.
- 142.
- 143.
- 144. Virtual Iraq 의다양한 시나리오 •시가지: 황량한 거리에 낡은 건물과 금방 무너질 것만 같은 아파트, 창고, 모스크, 공장 등이 있는 상황. 인적이나 교통 량이 거의 없는 버전과, 사람과 교통량이 많은 두 가지 버전 •시가지 빌딩 내부: 시가지의 일부 빌딩은 환자가 내부로 들 어가볼 수 있도록 내부 구조가 모델링. 빌딩은 비어있게 할 수도 있고, 적거나 많은 거주자가 내부에 있도록 설정 가능 •검문소: 시가지 시나리오의 일부로, 차량이 도시로 진입하 기 위해 정지하는 검문소 상황. •작은 시골 마을: 쓰러져가는 건물과 전투의 잔해들이 있는 작은 마을을 재현. 주변에 식물들이 많고, 건물들 사이로 멀 리 사막이 보임 •사막 기지: 군인들, 텐트, 군용 장비 등이 설치 되어 있는 사 막의 기지를 재현. •사막 도로: 비포장 도로의 환경. 각각 도시, 작은 시골 마을, 사막 기지 시나리오로 이어짐. 사막의 사구, 식물들, 낡은 건 물들, 전투 잔해, 길가의 사람 등으로 구성. Fig. 1. Outskirts of Virtual Iraq City Fig. 2. Center Area of Virtual Iraq City Fig. 3. Car Bombing in Virtual Iraq City User-Centered tests with the application were conducte the Naval Medical CenteroSan Diego and within an Army Combat Stress Control Team in Iraq (See Figure 8). This d at usability of the prototype system application that fed an iterative design process. A clinical trial version of the application built from this process is currently being tested with PTSD-diagnosed personnel at a variety of sites. The Fig. 4. Interior view from of Desert Road Humvee Scenario Fig. 5. Turret view from of Desert Road Humvee Scenario Fig. 6. IED Attack in Desert Road Humvee Scenario
- 146. 오즈의 마법사: 시각-촉각-청각-후각을통한 전쟁의 재현 • 상담사는 환자가 처해있는 모든 상황을 실시간으로 컨트롤 (‘오즈의 마법사’) • 환자가 실제 트라우마를 가진 상황을 최대한 비슷하게 재현 • 시각적, 청각적, 후각적, 촉각적 상황을 컨트롤 • 다양한 군용 차량 / 근처에 있는 건물, 차, 탱크 등을 폭파 • 비행기나 헬리콥터를 머리 위에 출현, 낮/밤, 비/안개 • 다양한 상황을 재현 가능 • 총격전이 벌어지거나, 매복에 당한 상황, 로켓포가 날아오는 상황 • 동료가 죽거나 부상을 입은 상황, 사람의 시체나 잔해를 본 상황 • 적군이나 민간인에게 총격을 가한 상황 등등
- 147. scores at baseline,post treatment and 3-month follow-up are in Fig group, mean Beck Anxiety Inventory scores significantly decrea (9.5) to 11.9 (13.6), (t=3.37, df=19, p < .003) and mean PHQ-9 decreased 49% from 13.3 (5.4) to 7.1 (6.7), (t=3.68, df=19, p < 0.00 Figure 4. PTSD Checklist scores across treatment Figure 5. BAI and PH The average number of sessions for this sample was just under successful treatment completers had documented mild and mode injuries, which suggest that this form of exposure can be useful PTSD Checklist scores across treatment • 연구 결과 20명의 환자들은 전반적으로 유의미한 개선을 보임 • 환자들 전체의 PCL-M 수치가 평균 54.4에서 35.6으로 감소 • 20명 중 16명은 치료 직후에 더 이상 PTSD 를 가지지 않은 것으로 나타남 • 치료가 끝난지 3개월 후에 환자들의 상태는 유지 http://www.ncbi.nlm.nih.gov/pubmed/19377167
- 148. reatment and 3-monthfollow-up are in Figure 4. For this same iety Inventory scores significantly decreased 33% from 18.6 =3.37, df=19, p < .003) and mean PHQ-9 (depression) scores 3 (5.4) to 7.1 (6.7), (t=3.68, df=19, p < 0.002) (see Figure 5). ores across treatment Figure 5. BAI and PHQ-Depression scores r of sessions for this sample was just under 11. Also, two of the mpleters had documented mild and moderate traumatic brain that this form of exposure can be usefully applied with this BAI and PHQ-Depression scores • 벡 불안 지수는 평균 18.6에서 11.9로 33% 감소 • PHQ-9 우울증 지수 역시 13.3에서 7.1로 49% 감소 • 경미한 외상성 뇌손상 (traumatic brain injury) 환자 2명에도 유의미한 효과 http://www.ncbi.nlm.nih.gov/pubmed/19377167
- 149. • Puretech Health •‘새로운 개념의 제약회사’를 추구하는 회사 • 기존의 신약 뿐만 아니라, 게임, 앱 등을 이용한 Digital Therapeutics 를 개발 • Digital Therapeutics는 최근 미국 FDA의 de novo 승인을 받기도 함
- 152. • Puretech Health •신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
- 153. • Puretech Health •신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
- 154. • Puretech Health •신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
- 156. LETTER doi:10.1038/nature12486 Video gametraining enhances cognitive control in older adults J. A. Anguera1,2,3 , J. Boccanfuso1,3 , J. L. Rintoul1,3 , O. Al-Hashimi1,2,3 , F. Faraji1,3 , J. Janowich1,3 , E. Kong1,3 , Y. Larraburo1,3 , C. Rolle1,3 , E. Johnston1 & A. Gazzaley1,2,3,4 Cognitivecontrolisdefinedbyasetofneuralprocessesthatallowusto interact with our complex environment in a goal-directed manner1 . Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generat- ing interference as the result of fundamental information processing limitations2 . It is clear that multitasking behaviour has become ubi- quitous in today’s technologically dense world3 , and substantial evid- ence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population4 . Here we show that multi- tasking performance, as assessed with a custom-designed three- dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalo- graphy,wereremediated by multitasking training (enhanced midline frontal theta power and frontal–posterior theta coherence). Critically, thistrainingresultedinperformancebenefitsthatextendedtountrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings high- light the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, ofhowacustom-designedvideogamecanbeusedtoassesscognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement. In a first experiment, we evaluated multitasking performance across the adult lifespan. A total of 174 participants spanning six decades of life (ages 20–79; ,30 individuals per decade) played a diagnostic version of NeuroRacertomeasuretheirperceptualdiscriminationability(‘signtask’) withandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see Supplementary Information for details of NeuroRacer). Performance was evaluated using two distinct game conditions: ‘sign only’ (respond as rapidly as possible to the appearance of a sign only when a green circle was present); and ‘sign and drive’ (simultaneously perform the sign task while maintaining a car in the centre of a winding road using a joystick (that is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was evaluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’ index was used to assess multitasking performance by calculating the percentage change in d9 from ‘sign only’ to ‘sign and drive’, such that greater cost (that is, a more negative percentage cost) indicates increased interference when simultaneously engaging in the two tasks (see Methods Summary). Prior to the assessment of multitasking costs, an adaptive staircase algorithm was used to determine the difficulty levels of the game at which each participant performed the perceptual discrimination and visuomotor tracking tasks in isolation at ,80% accuracy. These levels were then used to set the parameters of the component tasks in the multitasking condition, so that each individual played the game at a customizedchallengelevel.Thisensuredthatcomparisonswouldinform differences in the ability to multitask, and not merely reflect disparities in component skills (see Methods, Supplementary Figs 1 and 2, and Sup- plementary Information for more details). Multitasking performance diminished significantly across the adult lifespan in a linear fashion (that is, increasing cost, see Fig. 2a and Sup- plementaryTable1),withtheonlysignificantdifferenceincostbetween adjacent decades being the increase from the twenties (226.7% cost) to the thirties (238.6% cost). This deterioration in multitasking perform- ance is consistent with the pattern of performance decline across the lifespan observed for fluid cognitive abilities, such as reasoning5 and working memory6 . Thus, using NeuroRacer as a performance assess- ment tool, we replicated previously evidenced age-related multitasking deficits7,8 , and revealed that multitasking performance declines linearly as we advance in age beyond our twenties. In a second experiment, we explored whether older adults who trained by playing NeuroRacer in multitasking mode would exhibit improve- mentsintheirmultitaskingperformanceonthegame9,10 (thatis,diminished NeuroRacer costs). Critically, we also assessed whether this training 1 Department of Neurology, University of California, San Francisco, California 94158, USA. 2 Department of Physiology, University of California, San Francisco, California 94158, USA. 3 Center for Integrative Neuroscience, University of California, San Francisco, California 94158, USA. 4 Department of Psychiatry, University of California, San Francisco, California 94158, USA. 1 month MultitaskingSingle taskNo-contact control Initial visit NeuroRacer EEG and cognitive testing Drive only Sign only Sign and drive and 1 hour × 3 times per week × 1 month or Single task Multitask 6+ months Training intervention NeuroRacer or a b + + Figure 1 | NeuroRacer experimental conditions and training design. a, Screen shot captured during each experimental condition. b, Visualization of training design and measures collected at each time point. 5 S E P T E M B E R 2 0 1 3 | V O L 5 0 1 | N A T U R E | 9 7 Macmillan Publishers Limited. All rights reserved©2013
- 157. Video game trainingenhances cognitive control in older adults https://www.youtube.com/watch?v=1xPX8F_wl0c
- 158. transferred to enhancementsin their cognitive control abilities11 beyond those attained by participants who trained on the component tasks in isolation. In designing the multitasking training version of NeuroRacer, during game play as a key mechanistic feature of the tr In addition, although cost reduction was observed o group, equivalent improvement in component task sk byboth STTandMTT(seeSupplementary Figs 4 and that enhancedmultitaskingabilitywas notsolelyther component skills, but a function of learning to res generated by the two tasks when performed concurr the d9 cost improvement following training was not th trade-off, as driving performance costs also diminish group from pre- to post-training (see Supplementa Notably in the MTT group, the multitasking pe remained stable 6 months after training without boo 6 months, 221.9% cost). Interestingly, the MTT grou cost improved significantly beyond the cost level attai 20 year olds who played a single session of NeuroRac experiment 3; P , 0.001). Next, we assessed if training with NeuroRacer le enhancementsofcognitivecontrolabilitiesthatareknow in ageing (for example, sustained attention, divided a memory; see Supplementary Table 2)12 . We hypoth immersed in a challenging, adaptive, high-interferen for a prolonged period of time (that is, MTT) would cognitive performance on untrained tasks that also dem control. Consistent with our hypothesis, significant interactions and subsequent follow-up analyses eviden training improvements in both working memory (de task with and without distraction7 ; Fig. 3a, b) and su † –100% –90% –80% –70% –60% –50% –40% –30% –20% –10% Multitaskingcost(d′) † * ba 1 month later 6 months later Experiment 1: lifespan Experiment 2: training Single task training No-contact control Multitasking training 0% 20s 30s 40s 50s 60s 70s Initial Figure 2 | NeuroRacer multitasking costs. a, Costs across the lifespan (n 5 174) increased (that is, a more negative percentage) in a linear fashion when participants were grouped by decade (F(1,5) 5 135.7, P , 0.00001) or analysed individually (F(1,173) 5 42.8, r 5 0.45, P , 0.00001; see Supplementary Fig. 3), with significant increases in cost observed for all age groups versus the 20-year-old group (P , 0.05 for each decade comparison). b, Costs before training, 1 month post-training, and 6 months post-training showed a session X group interaction (F(4,72) 5 7.17, P , 0.0001, Cohen’s d 5 1.10), with follow-up analyses supporting a differential benefit for the MTT group (Cohen’s d for MTT vs STT 5 1.02; MTT vs NCC5 1.20). {P , 0.05 within group improvement from pre to post, *P , 0.05 between groups (n 5 46). Error bars represent s.e.m. –100 0 100 200 Pre–post WM task with distractions (RT) RTdifference(ms) † * a –100 0 100 200 Pre–p without d RTdifference(ms) † b RESEARCH LETTER Video game training enhances cognitive control in older adults z • 게임을 통한 고령층의 인지 능력 (멀티태스킹 능력) 개선 효과가 있음을 증명 • 60-85세 참가자 46명을 4주간 뉴로레이서를 통해서 훈련 • 그 결과 훈련 받지 않은 20대보다 더 잘 하게 되었으며, • 연습을 하지 않고 6개월이 지나도, 능력은 그대로 남아 있었다. Nature 501, 97–101 (2013)
- 159. Video game trainingenhances cognitive control in older adults (vigilance; test of variables of attention (T group (Fig. 3c; see Supplementary Table several statistical trendssuggestive of impro ance on other cognitive controltasks (dual- and changedetectiontask;see analysisofco in Supplementary Table 2). Note that alth and sustained attention improvements w rapid responses to test probes, neither im alternative version of the TOVA) nor accu cant group differences, revealing that traini of a speed/accuracy trade-off. Importantl ments were specific to working memory a cesses, and not theresult ofgeneralized incr as no group X session interactions were fou tasks (a stimulus detection task and the dig see Supplementary Table 2). Finally, only significant correlation between multitaski withNeuroRacer)andimprovementsonan task (delayed-recognition with distraction (Fig. 3d). These important ‘transfer of benefits’ sug lying mechanism of cognitive control was c MTT with NeuroRacer. To assess this furth basis of training effects by quantifying even tions (ERSP) and long-range phase coheren of each sign presented during NeuroRacer Wespecificallyassessedmidlinefrontalthe EEG measure of cognitive control (for exam tained attention15 and interference resolutio prefrontal cortex. In addition, we analysed between frontal and posterior brain region measure also associated with cognitive con memory14 and sustained attention15 ). Se power and coherence each revealed signifi b Long-range theta coherence Older adult post-training PLV (% coherence) 1 5 10 * ) Initial Older adults Younger adults † Midline frontal theta Power(dB) Initial * a Older adults Younger adults Older adult post-training Single task training Multitasking training No-contact control 3.40 3.05 2.70 2.35 1.65 1.30 0.95 0.60 0.25 –0.10 –0.45 –0.80 –1.15 –1.50 2.00 Nature 501, 97–101 (2013) • 인지 능력의 개선은 brain activity 로도 동일하게 관찰되었다. • 노년층 실험군에서 기술이 향상될수록 cognitive control을 관장하는 prefrontal cortex 의 activity가 높아지는 것이 관찰되었다.
- 160. OPEN ORIGINAL ARTICLE Characterizing cognitivecontrol abilities in children with 16p11.2 deletion using adaptive ‘video game’ technology: a pilot study JA Anguera1,2 , AN Brandes-Aitken1 , CE Rolle1 , SN Skinner1 , SS Desai1 , JD Bower3 , WE Martucci3 , WK Chung4 , EH Sherr1,5 and EJ Marco1,2,5 Assessing cognitive abilities in children is challenging for two primary reasons: lack of testing engagement can lead to low testing sensitivity and inherent performance variability. Here we sought to explore whether an engaging, adaptive digital cognitive platform built to look and feel like a video game would reliably measure attention-based abilities in children with and without neurodevelopmental disabilities related to a known genetic condition, 16p11.2 deletion. We assessed 20 children with 16p11.2 deletion, a genetic variation implicated in attention deficit/hyperactivity disorder and autism, as well as 16 siblings without the deletion and 75 neurotypical age-matched children. Deletion carriers showed significantly slower response times and greater response variability when compared with all non-carriers; by comparison, traditional non-adaptive selective attention assessments were unable to discriminate group differences. This phenotypic characterization highlights the potential power of administering tools that integrate adaptive psychophysical mechanics into video-game-style mechanics to achieve robust, reliable measurements. Translational Psychiatry (2016) 6, e893; doi:10.1038/tp.2016.178; published online 20 September 2016 INTRODUCTION Cognition is typically associated with measures of intelligence (for example, intellectual quotient (IQ)1 ), and is a reflection of one’s ability to perform higher-level processes by engaging specific mechanisms associated with learning, memory and reasoning. Such acts require the engagement of a specific subset of cognitive resources called cognitive control abilities,2–5 which engage the underlying neural mechanisms associated with atten- tion, working memory and goal-management faculties.6 These abilities are often assessed with validated pencil-and-paper approaches or, now more commonly with these same paradigms deployed on either desktop or laptop computers. These approaches are often less than ideal when assessing pediatric populations, as children have highly varied degree of testing engagement, leading to low test sensitivity.7–9 This is especially concerning when characterizing clinical populations, as increased performance variability in these groups often exceeds the range of testing sensitivity,7–9 limiting the ability to characterize cognitive deficits in certain populations. A proper assessment of cognitive control abilities in children is especially important, as these abilities allow children to interact with their complex environment in a goal-directed manner,10 are predictive of academic performance11 and are correlated with overall quality of life.12 For pediatric clinical populations, this characterization is especially critical as they are often assessed in an indirect fashion through intelligence quotients, parent report questionnaires13 and/or behavioral challenges,14 each of which fail to properly characterize these abilities in a direct manner. One approach to make testing more robust and user-friendly is to present material in an optimally engaging manner, a strategy particularly beneficial when assessing children. The rise of digital health technologies facilitates the ability to administer these types of tests on tablet-based technologies (that is, iPad) in a game-like manner.15 For instance, Dundar and Akcayir16 assessed tablet- based reading compared with book reading in school-aged children, and discovered that students preferred tablet-based reading, reporting it to be more enjoyable. Another approach used to optimize the testing experience involves the integration of adaptive staircase algorithms, as the incorporation of such appro- aches lead to more reliable assessments that can be completed in a timely manner. This approach, rooted in psychophysical research,17 has been a powerful way to ensure that individuals perform at their ability level on a given task, mitigating the possi- bility of floor/ceiling effects. With respect to assessing individual abilities, the incorporation of adaptive mechanics acts as a normalizing agent for each individual in accordance with their underlying cognitive abilities,18 facilitating fair comparisons between groups (for example, neurotypical and study populations). Adaptive mechanics in a consumer-style video game experi- ence could potentially assist in the challenge of interrogating cognitive abilities in a pediatric patient population. This synergistic approach would seemingly raise one’s level of engagement by making the testing experience more enjoyable and with greater sensitivity to individual differences, a key aspect typically missing in both clinical and research settings when testing these populations. Video game approaches have previously been utilized in clinical adult populations (for example, stroke,19,20 1 Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; 2 Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; 3 Akili Interactive Labs, Boston, MA, USA; 4 Department of Pediatrics, Columbia University Medical Center, New York, NY, USA and 5 Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA. Correspondence: JA Anguera or EJ Marco, University of California, San Francisco, Mission Bay – Sandler Neurosciences Center, UCSF MC 0444, 675 Nelson Rising Lane, Room 502, San Francisco, CA 94158, USA. E-mail: joaquin.anguera@ucsf.edu or elysa.marco@ucsf.edu Received 6 March 2016; revised 13 July 2016; accepted 18 July 2016 Citation: Transl Psychiatry (2016) 6, e893; doi:10.1038/tp.2016.178 www.nature.com/tp Figure 2. Project: EVO selective attention performance. (a) EVO single- and multi-tasking response time performance f non-affected siblings and non-affected control groups). (b) EVO multi-tasking RT. (c) Visual search task performance Characterizing cognitive control abilities in child JA Anguera et al •Project EVO (게임)을 통해서, •아동 집중력 장애(attention disorder) 관련 특정 유전형 carrier 를 골라낼 수 있음 •게임에서의 Response Time을 기준으로 carrier vs. non-carrier 간 유의미한 차이
- 161. RESEARCH ARTICLE A pilotstudy to determine the feasibility of enhancing cognitive abilities in children with sensory processing dysfunction Joaquin A. Anguera1,2☯ *, Anne N. Brandes-Aitken1☯ , Ashley D. Antovich1 , Camarin E. Rolle1 , Shivani S. Desai1 , Elysa J. Marco1,2,3 1 Department of Neurology, University of California, San Francisco, United States of America, 2 Department of Psychiatry, University of California, San Francisco, United States of America, 3 Department of Pediatrics, University of California, San Francisco, United States of America ☯ These authors contributed equally to this work. * joaquin.anguera@ucsf.edu Abstract Children with Sensory Processing Dysfunction (SPD) experience incoming information in atypical, distracting ways. Qualitative challenges with attention have been reported in these children, but such difficulties have not been quantified using either behavioral or functional neuroimaging methods. Furthermore, the efficacy of evidence-based cognitive control inter- ventions aimed at enhancing attention in this group has not been tested. Here we present work aimed at characterizing and enhancing attentional abilities for children with SPD. A sample of 38 SPD and 25 typically developing children were tested on behavioral, neural, and parental measures of attention before and after a 4-week iPad-based at-home cognitive remediation program. At baseline, 54% of children with SPD met or exceeded criteria on a parent report measure for inattention/hyperactivity. Significant deficits involving sustained attention, selective attention and goal management were observed only in the subset of SPD children with parent-reported inattention. This subset of children also showed reduced midline frontal theta activity, an electroencephalographic measure of attention. Following the cognitive intervention, only the SPD children with inattention/hyperactivity showed both improvements in midline frontal theta activity and on a parental report of inattention. Notably, 33% of these individuals no longer met the clinical cut-off for inattention, with the parent- reported improvements persisting for 9 months. These findings support the benefit of a targeted attention intervention for a subset of children with SPD, while simultaneously highlighting the importance of having a multifaceted assessment for individuals with neuro- developmental conditions to optimally personalize treatment. Introduction Five percent of all children suffer from Sensory Processing Dysfunction (SPD)[1], with these individuals exhibiting exaggerated aversive, withdrawal, or seeking behaviors associated with sensory inputs [2]. These sensory processing differences can have significant and lifelong con- sequences for learning and social abilities, and are often shared by children who meet PLOS ONE | https://doi.org/10.1371/journal.pone.0172616 April 5, 2017 1 / 19 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Anguera JA, Brandes-Aitken AN, Antovich AD, Rolle CE, Desai SS, Marco EJ (2017) A pilot study to determine the feasibility of enhancing cognitive abilities in children with sensory processing dysfunction. PLoS ONE 12(4): e0172616. https://doi.org/10.1371/journal. pone.0172616 Editor: Jacobus P. van Wouwe, TNO, NETHERLANDS Received: October 5, 2016 Accepted: February 1, 2017 Published: April 5, 2017 Copyright: © 2017 Anguera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported by the Mickelson-Brody Family Foundation, the Wallace Research Foundation, the James Gates Family Foundation, the Kawaja-Holcombe Family Foundation (EJM), and the SNAP 2015 Crowd funding effort. •감각처리장애(SPD)를 가진 소아 환자 중 ADHD를 가진 20명에 대해서 실험 •4주 동안 (주당 5일, 25분)Project EVO 게임을 하게 한 결과, •20명 중 7명이 큰 개선을 보여서 더 이상 ADHD의 범주에 들지 않게 됨 •사용 후 적어도 9개월 동안 효과가 지속되었음 Fig 4. Transfer effect on behavioral and parent report measures. Pre and post (A) response time (B) and resp revealing within group change. Error bars indicate standard error of the mean. Within group main effects of session = p .05, ** =.p .01. Sun symbols indicate statistically significant instances where SPD+IA post-training performa TDC group prior to training. (C) Vanderbilt parent report inattention change bar plot (calculated by pre-post margina significant group x session interaction. Error bars indicate standard error of the mean. All group x session interactio stars (* = p .05, ** =.p .01) on bar graph. https://doi.org/10.1371/journal.pone.0172616.g004 PLOS ONE | https://doi.org/10.1371/journal.pone.0172616 April 5, 2017
- 162. •ADHD에 대해서는 대규모RCT phase III 임상 시험 진행 중이며, FDA 의료기기 인허가 목표 •8-12살 환자(n=330), 치료 효과 없는 비디오게임을 control group으로 •primary endpoint: TOVA •의사의 처방을 받는 ADHD 치료용 게임 + 보험사의 커버 목표
- 163. • Woebot, 정신상담 챗봇 • 실제 상담사들이 하듯이, 대화형으로 설명하고 사용자의 정신 건강 상태를 체크 • 대부분 설문과 다를 것이 없지만 (정해진 답 중에 하나 선택), UI 상의 혁신이라고 볼 수 있음 • 아직까지는 아주 정교한 NLP를 사용하고 있지는 않음 (세션 당 한 번 정도)
- 164. • Woebot, 정신상담 챗봇 • 실제 상담사들이 하듯이, 대화형으로 설명하고 사용자의 정신 건강 상태를 체크 • 대부분 설문과 다를 것이 없지만 (정해진 답 중에 하나 선택), UI 상의 혁신이라고 볼 수 있음 • 아직까지는 아주 정교한 NLP를 사용하고 있지는 않음 (세션 당 한 번 정도)
- 165.
- 166. 우울증 치료 임상결과 1 임상 기간 : 2014년 10월 ~ 2016년 12월 N=96, 1회 30분 자극 Severe Moderate Mild 10 20 30 40 Beck Depression Inventory II 6주 42회 연속 복용SSRI Ybrain 5회 1회 1회5회 0 10 20 30 40 BASELINE 2 WEEK 4 WEEK 6 WEEK MADRS 6주 42회 연속 복용 Ybrain SSRI 5회 1회 1회5회 Severe Moderate Mild None Primary Outcome: 몽고메리-아스퍼그 우울평가척도(MADRS) Secondary Outcome: Beck 우울 척도(Beck Depression Inventory II) Courtesy of 이기원 대표님, YBrain •국내 96명 환자를 대상으로 2년간 double-blinded randomised 임상 연구 실시 •실험군: 가짜 약+ 진짜 자극기기 •대조군: 진짜 약 + 가짜 자극기기 •Primary Outcome인 MADRS 스케일에서 기기가 약에 조금 못 미치는 결과
- 167. 우울증 치료 임상결과 1 임상 기간 : 2014년 10월 ~ 2016년 12월 N=96, 1회 30분 자극 Severe Moderate Mild 10 20 30 40 Beck Depression Inventory II 6주 42회 연속 복용SSRI Ybrain 5회 1회 1회5회 0 10 20 30 40 BASELINE 2 WEEK 4 WEEK 6 WEEK MADRS 6주 42회 연속 복용 Ybrain SSRI 5회 1회 1회5회 Severe Moderate Mild None Primary Outcome: 몽고메리-아스퍼그 우울평가척도(MADRS) Secondary Outcome: Beck 우울 척도(Beck Depression Inventory II) Courtesy of 이기원 대표님, YBrain •Primary Outcome인 MADRS에서 기존 약물에 비해서 약간 효능이 적게 나옴 •Secondary Outcome인 BDI 에 대해서는 기존 약물과 동등하게 나옴 •이러한 결과에 따라서 식약처에서 ‘3등급 보조의료기기’ 로 인허가 •따라서, 원칙적으로는 기존에 우울증 약을 복용하는 환자를 대상으로 사용하게 될 것임
- 168. •경두개 직류자극치료술(tDCS) •2017년 3월국내 최초로 식약처의 3등급 보조의료기기 허가 •7월에는 유럽 CE허가를 받을 예정 •2~3년 내 FDA 허가를 받는 것을 목표 •추가 임상 연구 예정 •우울증 •독거 노인 우울증 치료 시범 사업 진행 중 •10월부터 하버드 의대와 아시아 지역 500명 대상의 임상 예정 •경도인지장애 임상 예정 •조현병 1차 임상 마무리 + 논문 출판 예정 •신의료기술평가 진행 예정
- 170. RespeRate • FDA 승인받은 유일한 비약물 고혈압 치료법 • sessions of therapeutic breathing 을 통해서 혈압 강하 효과 • 15분씩 일주일에 a few times 활용하면 significant blood pressure reduction 증명 • 전세계 25만 명 이상 사용
- 171.
- 172. 2breathe • 디지털 기기중, 수면 유도 목적으로는 2breathe가 유일 • 고혈압 치료기기의 ‘부작용’으로 수면 유도 효과 발견 • 안전성은 수십만 명의 환자에게 임상 시험 통해서 증명 • 교감신경의 활성화를 줄임으로써 사용자의 릴렉스와 수면을 유도
- 173.
- 174.
- 175. 수면 장애 불면증 호흡장애 기면증, 하지불안증후군… 스트레스 생활습관 (운동, 음식 등) 원인 불명 비약물치료 약물치료 수면 무호흡 코골이 코골이 환자의 75%가 수면 무호흡 증후군 무호흡: 호흡이 10초 이상 정지 저호흡: 호흡량 50% 이하 감소, 산소 포화도 4% 이상 저하 수면 무호흡 증후군: 무호흡이 시간 당 5회 이상, 7시간당 30회 이상 중추성: 뇌의 호흡 신호 전달 이상 폐쇄성: 좁은 상기도가 물리적으로 폐쇄 혼합성: 중추성+폐쇄성 수면의 질 하락 피로, 주간 졸림, 우울증, 두통 부정맥, 고혈압, 심장질환, 폐질환 비수술적 치료: 체중감량, 생활습관 개선, 약물치료, 구강내 장치 수술적 치료: 비강수술, 인두부 수술, 설 부 축소수술 등
- 176. 수면 관련 디지털 헬스 스마트폰 디바이스 SleepCycle, 백색 소음 앱 … etc 웨어러블 손목/손가락 머리 복부 Fitbit, Mi band, Basis Peak (팔찌), Oura Ring (반지) Zeo, 프라센 2breathe 침대 관련 매트리스/배게 아래 침대 Beddit,EarlySense (SleepSense), Nora MODD, Sleep Number, Tempur 침실 기기 Withings Aura, Resmed S+
- 177.
- 179. 슬립셋 •백색 소음 등을들려주며 정신적 안정을 유도 •의학적으로 효과가 증명되었다고 보기는 어려움 •수면리포트 •불면증, 스트레스, 육아태교, 학습능력
- 180. Fitbit •Fitbit 을 포함한대부분의 활동량 측정계는 수면 모니터링 기능을 포함 •움직임 (가속도계, 자이로미터) 및 심박수를 기반으로 모니터링 •따라서, 수면 모니터링은 정확하지 않으며, PSG와의 동등성 역시 증명된 바 없음
- 181. Fitbit •움직임 (가속도계, 자이로미터)및 심박수를 기반으로 모니터링 •자동 수면 모드, 총 수면 시간 •수면 - 뒤척임 - 깨어남 (3단계로 구분)
- 182.
- 183.
- 184. •수면 안대를 이용해서뇌파, 심박수, 호흡, 온도, 안구, 안면 근육 움직임 등을 측정 •깊은 수면, 얕은 수면, REM 수면을 구분 •색 패턴(LED)과 소리를 활용해서 렘수면과 비렘수면(NREM)의 변환 유도 •낮은 주파수의 입체음향을 제공함으로써 깊은 수면 뇌파를 유도 •임상적으로 증명되지는 않은 것으로 알고 있음; 투자 유치에 난항 프라센
- 185. ResMed S+ •비접촉식 수면모니터링 센서 •침대 근처에 두면 진동, 소리 등을 통해 수면을 모니터링 •Deep - Light - REM sleep - Disruption - Onset 을 구분 •의료기기는 아님; PSG와의 일치 여부 역시 알려진 바 없음
- 186. Table 1 모니터링 치료/개선 불면증 호흡관련 (sleepinduction) (수면무호흡, 코골이) Sleep Cycle 백색소음 Fitbit, Miband, 기어S Oura Ring Zeo Beddit,EarlySense ResMed S+ 백색 소음 프라센 2breathe MODD Sleep Cycle Beddit,EarlySense 2breathe 증명되지 않음 데이터 있음 정확도 근거 부재 PSG와 일치 x 수면 무호흡, 코골이 측정이 목적은 아니지 만, 호흡과 소리 측정 Nora Saver bed Balluga etc •모니터링 만으로는 효용의 제공이 어려움 •So What 문제 봉착: 모니터링 ➞ 치료/개선으로 이어질 수 있어야 함 •모니터링의 경우: 데이터의 정확성 자체의 담보가 어려움; PGS와 일치하지 않음 •치료/개선의 경우 •효과/안전성의 입증 필요 •의료기기/비의료기기 선택
- 188. (임상 연구 파트너를찾고 있다고 합니다)
- 189.
- 190. 모바일 헬스에 대한FDA의 가이드라인 •2011년 7월: 모바일 의료 어플리케이션 가이드라인 초안 •2013년 10월: 업데이트 된 최종 가이드라인 제시 •2015년 1월: 웰니스 목적의 위험도가 낮은 기기에 대한 가이드라인
- 191. 모바일 헬스에 대한FDA의 가이드라인 모든 앱과 기기들이 FDA 규제를 적용 받아야 하는 것은 아니나, 그 기능이 제대로 작동하지 않을 경우 소비자들의 건강을 위협할 수도 있는 앱과 기기는 기존의 의료용 기기가 받았던 것과 같은 엄격한 수준의 규제를 적용한다. 의료 기기/앱의 경우에도 리스크가 높지 않으면 규제 받지 않을 수 있다.
- 192. http://www.nature.com/news/mental-health-there-s-an-app-for-that-1.19694 • 정신 건강관리 앱은 잠재적인 리스크 정도를 쉽게 판단하기 어려운 경우가 많다. • 의료기기 / 웰니스의 불명확한 경계 • 질병을 예방, 치료, 진단해주는 앱 • ‘기분을 좋게 해준다’ 거나, ‘코칭’ 을 해주는 앱?
- 193.
- 195. The Journal ofClinical Investigation C L I N I C A L M E D I C I N E Introduction Clinical laboratory testing plays a critical role in health care and evidence-based medicine (1). Lab tests provide essential data that support clinical decisions to screen, diagnose, and treat health conditions (2). Most individuals encounter clinical testing through their health care provider during a routine health assess- ment or as a patient in a health care facility. However, individu- als are increasingly playing more active roles in managing their health, and some now seek direct access to laboratory testing for self-guided assessment or monitoring (3–5). IntheUSA,allclinicallaboratorytestingconductedonhumans is regulated by Centers for Medicare & Medicaid Services (CMS) based on guidelines outlined in Clinical Laboratory Improvement Amendments (CLIA) (6). To ensure analytical quality of labora- tory methods, certified laboratories are required to participate in periodic proficiency testing using a homogeneous batch of sam- ples that are distributed to each laboratory from a CMS-approved proficiency testing program. These programs assess the total allowable error (TEa) that combines method bias and total impre- cision for each analyte. Acceptability criteria are determined by CLIA and/or the appropriate accrediting agency (7). Direct-to-consumer service models now provide means for individuals to obtain laboratory testing outside traditional health care settings (4, 5). One company implementing this new model is Theranos, which offers a blood testing service that uses capillary tube collection and promises several advantages over traditional venipuncture: lower collection volumes (typically ≤150 μl versus ≥1.5 ml), convenience, and reduced cost — on average about 5-fold less than the 2 largest testing laboratories in the USA (Quest and LabCorp) (8). However, availability of these services varies by state, where access to offerings may be more or less restrictive BACKGROUND. Clinical laboratory tests are now being prescribed and made directly available to consumers through retail outlets in the USA. Concerns with these test have been raised regarding the uncertainty of testing methods used in these venues and a lack of open, scientific validation of the technical accuracy and clinical equivalency of results obtained through these services. METHODS. We conducted a cohort study of 60 healthy adults to compare the uncertainty and accuracy in 22 common clinical lab tests between one company offering blood tests obtained from finger prick (Theranos) and 2 major clinical testing services that require standard venipuncture draws (Quest and LabCorp). Samples were collected in Phoenix, Arizona, at an ambulatory clinic and at retail outlets with point-of-care services. RESULTS. Theranos flagged tests outside their normal range 1.6× more often than other testing services (P < 0.0001). Of the 22 lab measurements evaluated, 15 (68%) showed significant interservice variability (P < 0.002). We found nonequivalent lipid panel test results between Theranos and other clinical services. Variability in testing services, sample collection times, and subjects markedly influenced lab results. CONCLUSION. While laboratory practice standards exist to control this variability, the disparities between testing services we observed could potentially alter clinical interpretation and health care utilization. Greater transparency and evaluation of testing technologies would increase their utility in personalized health management. FUNDING. This work was supported by the Icahn Institute for Genomics and Multiscale Biology, a gift from the Harris Family Charitable Foundation (to J.T. Dudley), and grants from the NIH (R01 DK098242 and U54 CA189201, to J.T. Dudley, and R01 AG046170 and U01 AI111598, to E.E. Schadt). Evaluation of direct-to-consumer low-volume lab tests in healthy adults Brian A. Kidd,1,2,3 Gabriel Hoffman,1,2 Noah Zimmerman,3 Li Li,1,2,3 Joseph W. Morgan,3 Patricia K. Glowe,1,2,3 Gregory J. Botwin,3 Samir Parekh,4 Nikolina Babic,5 Matthew W. Doust,6 Gregory B. Stock,1,2,3 Eric E. Schadt,1,2 and Joel T. Dudley1,2,3 1 Department of Genetics and Genomic Sciences, 2 Icahn Institute for Genomics and Multiscale Biology, 3 Harris Center for Precision Wellness, 4 Department of Hematology and Medical Oncology, and 5 Department of Pathology, Icahn School of Medicine at Mount Sinai, NewYork, NewYork, USA. 6 Hope Research Institute (HRI), Phoenix, Arizona, USA. Conflict of interest: J.T. Dudley owns equity in NuMedii Inc. and has received consulting fees or honoraria from Janssen Pharmaceuticals, GlaxoSmithKline, AstraZeneca, and LAM Therapeutics. Role of funding source: Study funding provided by the Icahn Institute for Genomics and Multiscale Biology and the Harris Center for Precision Wellness at the Icahn School of Medicine at Mount Sinai. Salaries of B.A. Kidd, J.T. Dudley, and E.E. Schadt Downloaded from http://www.jci.org on March 28, 2016. http://dx.doi.org/10.1172/JCI86318 •Mt Sinai 에서 내어놓은 Theranos 의 정확도에 대한 논문 •2015년 7월 경에 60명의 건강한 환자들을 대상으로 5일 간에 걸쳐서 •22가지의 검사 항목을 테라노스와 또 다른 두 군데의 검사 기관에 맡겨서 결과를 비교 •결론적으로 Theranos의 결과가 많이 부정확 •콜레스테롤 등의 경우는 의사의 진단이 바뀔 정도로 크게 부정확 •전반적인 테스트들 결과 정상 범위가 아니라고 판단하는 경우가 테라노스가 1.6배 많음 •22개의 검사 항목 중에서 15개에서 유의미하게 결과의 차이가 있었습니다. •논문에서는 알 수 없는 또 다른 문제 •Theranos가 자체적으로 개발했다고 '주장' 했던 에디슨 기기를 정말로 썼느냐...하는 것 •WSJ 에 나온 과거 직원의 증언에 따르면, 이미 2015년 7월경이라면, •에디슨 기기를 쓰지 않고 지멘스 등 기존 다른 기기에 혈액을 희석해서 쓰고 있을 때 •역시나(?) 이번에도 테라노스는 conflict-of-interest 가 있는 잘못된 논문이라는 반응
- 196. 디지털 헬스케어 전반에서는 근거의중요성을 인식하고 있고, 좋은 디자인의 임상연구도 증가하고 있다.
- 197. www.nature.com/scientificreports Successful weight reduction andmaintenance by using a smartphone application in those with overweight and obesity SangOukChin1,* ,Changwon Keum2,* , JunghoonWoo3 , Jehwan Park2 , Hyung JinChoi4 , Jeong-taekWoo5 & SangYoul Rhee5 A discrepancy exists with regard to the effect of smartphone applications (apps) on weight reduction due to the several limitations of previous studies.This is a retrospective cohort study, aimed to investigate the effectiveness of a smartphone app on weight reduction in obese or overweight individuals, based on the complete enumeration study that utilized the clinical and logging data entered by NoomCoach app users betweenOctober 2012 andApril 2014.A total of 35,921 participants were included in the analysis, of whom 77.9% reported a decrease in body weight while they were using the app (median 267 days; interquartile range=182). Dinner input frequency was the most important factor for successful weight loss (OR=10.69; 95%CI=6.20–19.53; p<0.001), and more frequent input of weight significantly decreased the possibility of experiencing the yo-yo effect (OR=0.59, 95%CI=0.39–0.89; p<0.001).This study demonstrated the clinical utility of an app for successful weight reduction in the majority of the app users; the effects were more significant for individuals who monitored their weight and diet more frequently. Obesity is a global epidemic with a rapidly increasing prevalence worldwide1,2 . As obese individuals experience significantly higher mortality when compared with the non-obese population3,4 , this phenomenon poses a sig- nificant socioeconomic burden, necessitating strategies to manage overweight and prevent obesity5 . Although numerous interventions such as life style modification including exercise6–10 , and pharmacotherapy11–13 have been shown effective for both the prevention and treatment of obesity, some of these methods were found to have a limitation which required substantial financial inputs and repeated time-consuming processes14,15 . Recently, as the number of smartphone users is increasing dramatically, many investigators have attempted to implement smartphone applications (app) for health promotion16–19 . Consequently, many smartphone apps have demonstrated at least partial efficacy in promoting successful weight reduction according to the number of previous studies20–24 . However, due to the limitations associated with study design such as small-scale studies and short investigation periods, a discrepancy exists with regard to the effect of apps on weight reduction20,21,23 . Even systemic reviews which investigated the efficacy of mobile apps for weight reduction reported more or less inconsistent results; Flores Mateo et al. reported a significant weight loss by mobile phone app intervention when compared with control groups25 whereas Semper et al. reported that four of the six studies included in the analysis showed no significant difference of weight reduction between comparison groups26 . Thus, the aim of this study was to investigate the effectiveness of a smartphone app on weight reduction in obese or overweight individuals Recei e : 0 pri 016 Accepte : 15 eptem er 016 Pu is e : 0 o em er 016 OPEN •스마트폰 앱이 체중 감량에 도움을 줄 수 있는가? •2012년부터 2014년 까지 최소 6개월 이상 애플리케이션을 사용 •80여 국가(미국, 독일, 한국, 영국, 일본 등)에서 모집된 35,921명의 데이터 •애플리케이션 평균 사용기간은 267일 Chin et al. Sci Rep 2016 스마트폰 애플리케이션의 체중 감량 효과 입증에 관한 논문
- 198. 1SCIENTIFIC REPORTS |(2018) 8:3642 | DOI:10.1038/s41598-018-22034-0 www.nature.com/scientificreports The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders:A randomized, controlled, open-label study DaYoung Lee1,2 , Jeongwoon Park3 , DooahChoi3 , Hong-YupAhn4 , Sung-Woo Park1 & Cheol-Young Park 1 This randomized, controlled, open-label study conducted in Kangbuk Samsung Hospital evaluated the effectiveness, reproducibility, and durability of tailored mobile coaching (TMC) on diabetes management.The participants included 148 Korean adult policyholders with type 2 diabetes divided into the Intervention-Maintenance (I-M) group (n=74) andControl-Intervention (C-I) group (n=74). Intervention was the addition ofTMC to typical diabetes care. In the 6-month phase 1, the I-M group receivedTMC, and theC-I group received their usual diabetes care. During the second 6-month phase 2, theC-I group receivedTMC, and the I-M group received only regular information messages.After the 6-month phase 1, a significant decrease (0.6%) in HbA1c levels compared with baseline values was observed in only the I-M group (from 8.1±1.4% to 7.5±1.1%, P<0.001 based on a paired t-test). At the end of phase 2, HbA1c levels in theC-I group decreased by 0.6% compared with the value at 6 months (from 7.9±1.5 to 7.3±1.0, P<0.001 based on a paired t-test). In the I-M group, no changes were observed. Both groups showed significant improvements in frequency of blood-glucose testing and exercise. In conclusion, addition ofTMC to conventional treatment for diabetes improved glycemic control, and this effect was maintained without individualized message feedback. The incidence and prevalence of type 2 diabetes are increasing rapidly worldwide, and the disease is expected to affect 439 million adults by 20301 . Previous large clinical trials indicated that adequate glycemic control con- tributed to a reduction in both microvascular and macrovascular complications as well as mortality rates due to diabetes2,3 . Complications from diabetes result in greater expenditure and reduced productivity. Therefore, it is a socioeconomic concern4,5 . Adequate glycemic control is important not only as an individual health problem, but also as a challenge to healthcare systems worldwide. However, approximately 40% of subjects with diabetes in the United States do not meet the recommended target for glycemic control, low-density lipoprotein cholesterol (LDL-C) level, or blood pressure (BP)6 . In Korea, glycated hemoglobin (HbA1c) levels for nearly half of diabetic patients were above 7.0%7 . Although successful diabetes care requires therapeutic lifestyle modification in addition to proper medica- tion8–10 , only 55% of individuals with type 2 diabetes receive diabetes education from healthcare professionals11 , and 16% report adhering to recommended self-management activities9 . Multifaceted professional inter- ventions are needed to support patient efforts for behavior change including healthy lifestyle choices, disease self-management, and prevention of diabetes complications10 . 1 Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, SungkyunkwanUniversitySchool of Medicine,Seoul, Republic of Korea.2 Division of Endocrinology and Metabolism, Department of Internal Medicine, KoreaUniversityCollege of Medicine,Seoul, Republic of Korea.3 Huraypositive Inc. Sinsa-dong, Gangnam-gu, Seoul, Republic of Korea. 4 Department of Statistics, Dongguk University-Seoul, Seoul, Republic of Korea. Correspondence and requests for materials should be addressed to C.-Y.P. (email: cydoctor@ chol.com) Received: 29 November 2017 Accepted: 15 February 2018 Published: xx xx xxxx OPEN 스마트폰 앱의 제2형 당뇨환자의 당화혈색소 감소에 관한 논문
- 199. 스마트폰 앱의 당뇨병예방 효과 입증을 위한 임상연구
- 200. LETTER doi:10.1038/nature12486 Video gametraining enhances cognitive control in older adults J. A. Anguera1,2,3 , J. Boccanfuso1,3 , J. L. Rintoul1,3 , O. Al-Hashimi1,2,3 , F. Faraji1,3 , J. Janowich1,3 , E. Kong1,3 , Y. Larraburo1,3 , C. Rolle1,3 , E. Johnston1 & A. Gazzaley1,2,3,4 Cognitivecontrolisdefinedbyasetofneuralprocessesthatallowusto interact with our complex environment in a goal-directed manner1 . Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generat- ing interference as the result of fundamental information processing limitations2 . It is clear that multitasking behaviour has become ubi- quitous in today’s technologically dense world3 , and substantial evid- ence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population4 . Here we show that multi- tasking performance, as assessed with a custom-designed three- dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalo- graphy,wereremediated by multitasking training (enhanced midline frontal theta power and frontal–posterior theta coherence). Critically, thistrainingresultedinperformancebenefitsthatextendedtountrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings high- light the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, ofhowacustom-designedvideogamecanbeusedtoassesscognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement. In a first experiment, we evaluated multitasking performance across the adult lifespan. A total of 174 participants spanning six decades of life (ages 20–79; ,30 individuals per decade) played a diagnostic version of NeuroRacertomeasuretheirperceptualdiscriminationability(‘signtask’) withandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see Supplementary Information for details of NeuroRacer). Performance was evaluated using two distinct game conditions: ‘sign only’ (respond as rapidly as possible to the appearance of a sign only when a green circle was present); and ‘sign and drive’ (simultaneously perform the sign task while maintaining a car in the centre of a winding road using a joystick (that is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was evaluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’ index was used to assess multitasking performance by calculating the percentage change in d9 from ‘sign only’ to ‘sign and drive’, such that greater cost (that is, a more negative percentage cost) indicates increased interference when simultaneously engaging in the two tasks (see Methods Summary). Prior to the assessment of multitasking costs, an adaptive staircase algorithm was used to determine the difficulty levels of the game at which each participant performed the perceptual discrimination and visuomotor tracking tasks in isolation at ,80% accuracy. These levels were then used to set the parameters of the component tasks in the multitasking condition, so that each individual played the game at a customizedchallengelevel.Thisensuredthatcomparisonswouldinform differences in the ability to multitask, and not merely reflect disparities in component skills (see Methods, Supplementary Figs 1 and 2, and Sup- plementary Information for more details). Multitasking performance diminished significantly across the adult lifespan in a linear fashion (that is, increasing cost, see Fig. 2a and Sup- plementaryTable1),withtheonlysignificantdifferenceincostbetween adjacent decades being the increase from the twenties (226.7% cost) to the thirties (238.6% cost). This deterioration in multitasking perform- ance is consistent with the pattern of performance decline across the lifespan observed for fluid cognitive abilities, such as reasoning5 and working memory6 . Thus, using NeuroRacer as a performance assess- ment tool, we replicated previously evidenced age-related multitasking deficits7,8 , and revealed that multitasking performance declines linearly as we advance in age beyond our twenties. In a second experiment, we explored whether older adults who trained by playing NeuroRacer in multitasking mode would exhibit improve- mentsintheirmultitaskingperformanceonthegame9,10 (thatis,diminished NeuroRacer costs). Critically, we also assessed whether this training 1 Department of Neurology, University of California, San Francisco, California 94158, USA. 2 Department of Physiology, University of California, San Francisco, California 94158, USA. 3 Center for Integrative Neuroscience, University of California, San Francisco, California 94158, USA. 4 Department of Psychiatry, University of California, San Francisco, California 94158, USA. 1 month MultitaskingSingle taskNo-contact control Initial visit NeuroRacer EEG and cognitive testing Drive only Sign only Sign and drive and 1 hour × 3 times per week × 1 month or Single task Multitask 6+ months Training intervention NeuroRacer or a b + + Figure 1 | NeuroRacer experimental conditions and training design. a, Screen shot captured during each experimental condition. b, Visualization of training design and measures collected at each time point. 5 S E P T E M B E R 2 0 1 3 | V O L 5 0 1 | N A T U R E | 9 7 Macmillan Publishers Limited. All rights reserved©2013 비디오 게임의 인지능력 향상 효과에 대한 논문
- 201. Original Paper Delivering CognitiveBehavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial Kathleen Kara Fitzpatrick1* , PhD; Alison Darcy2* , PhD; Molly Vierhile1 , BA 1 Stanford School of Medicine, Department of Psychiatry and Behavioral Sciences, Stanford, CA, United States 2 Woebot Labs Inc., San Francisco, CA, United States * these authors contributed equally Corresponding Author: Alison Darcy, PhD Woebot Labs Inc. 55 Fair Avenue San Francisco, CA, 94110 United States Email: alison@woebot.io Abstract Background: Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time. Objective: The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression. Methods: In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,” as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Results: Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants’ comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy. Conclusions: Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT. (JMIR Ment Health 2017;4(2):e19) doi:10.2196/mental.7785 KEYWORDS conversational agents; mobile mental health; mental health; chatbots; depression; anxiety; college students; digital health Introduction Up to 74% of mental health diagnoses have their first onset particularly common among college students, with more than half reporting symptoms of anxiety and depression in the previous year that were so severe they had difficulty functioning Fitzpatrick et alJMIR MENTAL HEALTH 챗봇의 우울증 개선 효과에 관한 논문
- 202. 우울증 치료 임상결과 1 임상 기간 : 2014년 10월 ~ 2016년 12월 N=96, 1회 30분 자극 Severe Moderate Mild 10 20 30 40 Beck Depression Inventory II 6주 42회 연속 복용SSRI Ybrain 5회 1회 1회5회 0 10 20 30 40 BASELINE 2 WEEK 4 WEEK 6 WEEK MADRS 6주 42회 연속 복용 Ybrain SSRI 5회 1회 1회5회 Severe Moderate Mild None Primary Outcome: 몽고메리-아스퍼그 우울평가척도(MADRS) Secondary Outcome: Beck 우울 척도(Beck Depression Inventory II) Courtesy of 이기원 대표님, YBrain 휴대용 의료기기의 우울증 완화 효과 임상
- 203. http://www.nature.com/news/mental-health-there-s-an-app-for-that-1.19694 • 정신 건강관리 앱은 잠재적인 리스크 정도를 쉽게 판단하기 어려운 경우가 많다. • 의료기기 / 웰니스의 불명확한 경계 • 질병을 예방, 치료, 진단해주는 앱 • ‘기분을 좋게 해준다’ 거나, ‘코칭’ 을 해주는 앱?
- 204. 수면다원검사 - fitbit •현재 시장에 출시된 수면모니터링 기기 중 PSG와 일치도를 검증한 것은 거의 없다.
- 205.
- 206. • 2013년 연구 •43%의 앱만이 프라이버시 정책을 가지고 있음 • 72%의 앱이 개인 프라이버시에 대한 중간 (32%), 혹은 높은 (40%) 위험도
- 207. RECEIVED 22 December2013 REVISED 7 July 2014 ACCEPTED 3 August 2014 PUBLISHED ONLINE FIRST 21 August 2014 Availability and quality of mobile health app privacy policies Ali Sunyaev1 , Tobias Dehling1 , Patrick L Taylor2 , Kenneth D Mandl3 ABSTRACT .................................................................................................................................................... Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of mHealth app privacy policies on iOS and Android. Over 35 000 mHealth apps are available for iOS and Android. Of the 600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was 1755 (SD 1301) words with a reading grade level of 16 (SD 2.9). Two thirds (66.1%) of privacy policies did not specifically address the app itself. Our findings show that currently mHealth developers often fail to provide app privacy policies. The privacy pol- icies that are available do not make information privacy practices transparent to users, require college-level literacy, and are often not focused on the app itself. Further research is warranted to address why privacy policies are often absent, opaque, or irrelevant, and to find a remedy. .................................................................................................................................................... INTRODUCTION Apple’s iOS and Google’s Android operating systems and asso- ciated application (app) stores, itunes.apple.com and play.goo- gle.com, are becoming the de facto global platforms for mobile health (mHealth).1,2 Recently, both platforms additionally announced the roll out of their own apps fostering app interop- erability and offering central storage for all mHealth apps and sensors of users’ devices.3,4 mHealth apps leverage a wide range of embedded technology in iOS and Android devices for collecting and storing personal data, including contacts and calendars, and patient-reported data as well as information col- lected with cameras and sensors, including location, accelera- tion, audio, or orientation. 5–7 Although patients value control of their personally identifiable data8,9 and the Federal Trade Commission10 recommends provision of privacy policies for mobile apps, little attention has been paid to the information security and privacy policies and practices of mHealth app ven- dors. Although both app stores retain the right to remove apps for infringements of privacy, neither has explicit policies addressing the information security and privacy of medical in- formation. Users choose among an ecosystem of substitutable mHealth apps11 and should have transparency as to which apps have privacy practices best aligned with their individual preferences. We sought to assess mHealth apps for the pres- ence and scope of privacy policies, and what information they offer. METHODS We surveyed (figure 1) the most frequently rated and thus pop- ular English language mHealth apps in the Apple iTunes Store and the Google Play Store. App stores organize their offerings in categories (eg, Books, Games, and News). We selected apps from the Medical and Health and Fitness categories offered in both stores in May 2013. The iOS app store lists all apps by category and offers the desired information in plain hypertext markup language (HTML), enabling us to automatically parse app information to extract data. On the other hand, the Android app store uses dynamically generated HTML pages so that the HTML texts displayed in the browser do not contain much use- ful information, which is dynamically loaded from an underlying database. Hence, we used a third-party open-source interface, the android-market-api (http://code.google.com/p/android- market-api), for retrieving app information. Upon initial review, many apps were not available in English, did not have an English description, or were not health-related, despite being offered in the categories Medical or Health and Fitness (eg, apps offering wallpapers). In order to exclude such apps from further assessment, we tagged all app descriptions with descriptive terms. The tags characterize health-related app functionality, access to information, and handling of information. We manually tagged 200 apps (100 Health and Fitness, 100 Medical) establishing an initial tag cor- pus and employed string matching12 to automatically tag the remaining apps. Apps not matched by at least four distinct tags were excluded from further assessment. Discovery and evaluation of privacy policies We used a three-step manual procedure for privacy policy dis- covery looking at typical locations for privacy policies. Privacy policies were abstracted from March 2013 to June 2013. First, we checked for a privacy policy on the app store web site for the particular app. Then we checked the web page maintained Correspondence to Professor Ali Sunyaev, Faculty of Management, Economics and Social Sciences, University of Cologne, Albertus-Magnus-Platz, Cologne 50923, Germany; sunyaev@wiso.uni-koeln.de BRIEFCOMMUNICATION Sunyaev A, et al. J Am Med Inform Assoc 2015;22:e28–e33. doi:10.1136/amiajnl-2013-002605, Brief Communication byguestonApril17,2016http://jamia.oxfordjournals.org/Downloadedfrom • 2014년 연구 • 600개의 앱 중에 183 개 (약 30%) 만이 프라이버시 정책을 가지고 있었음
- 208. Letters RESEARCH LETTER Privacy Policiesof Android Diabetes Apps and Sharing of Health Information Mobile health apps can help individuals manage chronic health conditions.1 One-fifth of smartphone owners had health apps in 2012,2 and 7% of primary care physicians rec- ommended a health app.3 The US Food and Drug Adminis- tration has approved the prescription of some apps.4 Health apps can transmit sensitive medical data, including disease status and medication compliance. Privacy risks and the relationship between privacy disclosures and practices of health apps are understudied. Methods | On January 3, 2014, we identified all Android dia- betes apps by searching Google Play using the term diabetes. Android is the most popular mobile operating system worldwide with 82.8% market share (compared with Apple iOS’s 13.9%).5 We collected and analyzed privacy policies and permissions (disclosures of what apps can access or control on the device) for apps that remained 6 months after our initial search. Because consumers may want to know about privacy protections before choosing an app, we deter- mined which apps had policies available predownload and what the policies protected. Then we installed a random subset of apps to determine whether data were transmitted to third parties, defined as any website not directly under the developer’s control, such as data aggregators or adver- tising networks. We performed χ2 tests of independence (Excel 2010, Microsoft) to determine whether apps with privacy policies were more likely to protect personal information than apps without privacy policies. A 2-sided P value less than .05 was considered significant. Results | We identified 271 diabetes apps and chose a random sample of 75 for the transmission analysis. Within 6 months, 60 apps became unavailable, leaving 211 apps in the sample and 65 apps in the subset. Most of the 211 apps (81%) did not have privacy policies. Of the 41 apps (19%) with privacy policies, not all of the provisions actually pro- tected privacy (eg, 80.5% collected user data and 48.8% shared data) (Table 1). Only 4 policies said they would ask users for permission to share data. Permissions, which users must accept to download an app, authorized collection and modification of sensitive information, including tracking location (17.5%), activating the camera (11.4%), activating the microphone (3.8%), and modifying or deleting information (64.0%) (Table 2). In the transmission analysis, sensitive health informa- tion from diabetes apps (eg, insulin and blood glucose lev- els) was routinely collected and shared with third parties, with 56 of 65 apps (86.2%) placing tracking cookies; 31 of the 41 apps (76%) without privacy policies, and 19 of 24 apps (79%) with privacy policies shared user information, which was not statistically significantly different (N = 65; Table 1. Privacy Policy Provisions for the 41 Apps With Privacy Policies (19% of the 211 Apps)a Type of Privacy Policy Provision Apps, No. (%) Personal Information Shared if required by law 25 (61.0) Collected when the app is used 21 (51.2) Collected when a user registers through an online account 21 (51.2) Stored in the developer’s system 18 (43.9) Only disclosed with the user’s consent 12 (29.3) Shared to improve service 11 (26.8) Shared with business partners 10 (24.4) Not sold 9 (22.0) No personal information from children collected 6 (14.6) User Data Collected 33 (80.5) Shared with partners and/or third parties 20 (48.8) May be used for advertisement purposes 16 (39.0) May be transferred to various countries around the world 11 (26.8) Electronic safeguards for data protection are used 22 (53.7) Cookies will be used 20 (48.8) Log files will be collected 7 (17.1) Aggregated User Data Does not contain personal information 19 (46.3) May be used to create statistics 15 (36.6) May be disclosed to advertisers 7 (17.1) User Options Can opt out of cookies 13 (31.7) Can opt out of receiving emails 9 (22.0) Can opt out of receiving marketing materials 6 (14.6) a Provisions reflect the language used in privacy policies. Less common provisions (n Յ5) were the following: no personal information from children under 13 years of age is collected without the consent of a parent; personal information will not be disclosed to third parties for direct marketing purposes; data will be shared only with permission from the user; data will not be shared; personal information will not be shared; health information is treated differently than other types of information; data will not be sold; information is collected during the app download process; personal information will be shared with advertisers; user can opt out of information transfer to third parties for marketing purposes; no data will be stored; aggregated user data may be disclosed to analytics and search engine providers; personal information will be shared with research organizations. jama.com (Reprinted) JAMA March 8, 2016 Volume 315, Number 10 1051 • 2015년 연구 • 총 211개의 앱 중에 41개 (19%)만 프라이버시 정책이 있음 • 이 41개 앱의 프라이버시 정책을 살펴보면, • 48%는 써드 파티에게 사용자 데이터를 공유함 • 61%는 법으로 필요한 경우는 데이터가 공개될 수 있음 • 43%는 개발자 시스템에 데이터가 저장 • 즉, 프라이버시 정책을 가지고 있는 경우에도, ‘보호하지 않는’ 정책인 경우가 상당수
- 209. Summary Recent advances andchallenges of digital mental healthcare • Digital Phenotype • AI for mental health • Digital Therapeutics • Sleep disorder • Limitations
- 211. Feedback/Questions • E-mail: yoonsup.choi@gmail.com •Blog: http://www.yoonsupchoi.com • Facebook: 최윤섭 디지털 헬스케어 연구소